Processing of large document collections

Fall 2002, Part 2

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Outline 1. Term selection: information gain 2. Character code issues 3. Text summarization

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1. Term selection a large document collection may contain

millions of words -> document vectors would contain millions of dimensions

many algorithms cannot handle high dimensionality of the term space (= large number of terms)

usually only a part of terms are used how to select terms that are used?

term selection (often called feature selection or dimensionality reduction) methods

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Term selection: information gain Information gain: measures the

(number of bits of) information obtained for category prediction by knowing the presence or absence of a term in a document

information gain is calculated for each term and the highest-scoring n terms are selected

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Term selection: IG information gain for a term t:

m

i

m

ii i i i

im

ii

t c P t c P t P t c P t c P t P

c P c P t G

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1

) |~ ( log ) |~ ( ) (~ ) | ( log ) | ( ) (

) ( log ) ( ) (

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Estimating probabilities Doc 1: cat cat cat (c) Doc 2: cat cat cat dog (c) Doc 3: cat dog mouse (~c) Doc 4: cat cat cat dog dog dog (~c) Doc 5: mouse (~c)

2 classes: c and ~c

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Term selection: estimating probabilities P(t): probability of a term t

P(cat) = 4/5, or ‘cat’ occurs in 4 docs of 5

P(cat) = 10/17 the proportion of the occurrences of ´cat’

of the all term occurrences

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Term selection: estimating probabilities P(~t): probability of the absence of

t P(~cat) = 1/5, or P(~cat) = 7/17

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Term selection: estimating probabilities P(ci): probability of category i

P(c) = 2/5 (the proportion of documents belonging to c in the collection), or

P(c) = 7/17 (7 of the 17 terms occur in the documents belonging to c)

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Term selection: estimating probabilities P(ci | t): probability of category i if t

is in the document; i.e., which proportion of the documents where t occurs belong to the category i P(c | cat) = 2/4 (or 6/10) P(~c | cat) = 2/4 (or 4/10) P(c | mouse) = 0 P(~c | mouse) = 1

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Term selection: estimating probabilities P(ci | ~t): probability of category i

if t is not in the document; i.e., which proportion of the documents where t does not occur belongs to the category i P(c | ~cat) = 0 (or 1/7) P(c | ~dog) = ½ (or 6/12) P(c | ~mouse) = 2/3 (or 7/15)

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Term selection: estimating probabilities

In other words... Let

term t occurs in B documents, A of them are in category c

category c has D documents, of the whole of N documents in the collection

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Term selection: estimating probabilities

For instance, P(t): B/N P(~t): (N-B)/N P(c): D/N P(c|t): A/B P(c|~t): (D-A)/(N-B)

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Term selection: IG information gain for a term t:

G(cat) = -0.40 G(dog) = -0.38 G(mouse) = -0.01

m

i

m

ii i i i

im

ii

t c P t c P t P t c P t c P t P

c P c P t G

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1

) |~ ( log ) |~ ( ) (~ ) | ( log ) | ( ) (

) ( log ) ( ) (

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2. Character code issues Abstract character vs. its

graphical representation (glyph, font)

abstract characters are grouped into alphabets each alphabet forms the basis of the

written form of a certain language or a set of languages

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Character codes For instance

for English: uppercase letters A-Z lowercase letters a-z punctuation marks digits 0-9 common symbols: +, =

ideographic symbols of Chinese and Japanese

phonetic letters of Western languages

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Some terminology character repertoire (merkkivalikoima)

a set of distinct characters, alphabet no internal presentation, ordering etc

assumed usually defined by specifying names of

characters and a sample presentation of characters in visible form

repertoire may contain characters which look the same (in some presentations), but are logically distinct

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Some terminology character code (merkkikoodi)

a mapping which defines a one-to-one correspondence between characters in a character repertoire and a set of nonnegative integers

each character is assigned a unique code position (code number, code value, code element, code point, code set value, code)

set of codes often has ”holes”

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Some terminology character encoding (merkkikoodaus)

an algorithm for presenting characters in digital form by mapping sequences of code numbers into sequences of octets (=bytes)

in the simplest case, each character is mapped to an integer in the range 0-255 according to a character code and these are used as octets

works only for character repertoires with at most 256 characters

for larger sets, more complicated encodings are needed

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Character codes in English:

26 letters in both lower- and uppercase ten digits + some punctuation marks

in Russian: cyrillic letters both could use the same set of code

points (if not a bilingual document) in Japanese: could be over 6000

characters

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Character codes: standars Character codes can be arbitrary,

but in practice standardization is needed for interoperability (between computers, programs,...)

early standards were designed for English only, or for a small group of languages at a time

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Character codes: standards

ASCII ISO-8859 (e.g. ISO Latin1) Unicode UTF-8, UTF-16

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ASCII American Standard Code for Information

Interchange A seven bit code -> 128 code positions actually 95 printable characters only

code positions 0-31 and 127 are assigned to control characters (mostly outdated)

ISO 646 (1972) version of ASCII incorporated several national variants (accented letters and currency symbols) e.g. @[\]{|} replaced

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ASCII With 7 bits, the set of code points is too

small for anything else than American English

solution: 8 bits brings more code points (256) ASCII character repertoire is mapped to the

values 0-127 additional symbols are mapped to other

values

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Extended ASCII Problems:

different manufacturers each developed their own 8-bit extensions to ASCII

different character repertoires -> translation between them is not always possible

also 256 code values is not enough to represent all the alphabets -> different variants for different languages

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ISO 8859 Standardization of 8-bit character sets In the 80´s: multipart standard ISO 8859

was produced defines a collection of 8-bit character sets,

each designed for a group of languages the first part: ISO 8859-1 (ISO Latin1)

covers most Western European languages 0-127: identical to ASCII, 128-159 (mostly)

unused, 96 code values for accented letters and symbols

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”Safe” ASCII subset due to the national variants, only

the following characters can be regarded ”safe” in data transmission: A-Z, a-z, 0-9 ! ” % & ’ ( ) * + , - . / : ; < = > ?

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Unicode 256 is not enough code positions

for ideographically represented languages (Chinese, Japanese…)

for simultaneous use of several languages

solution: more than one byte for each code value

a 16-bit character set has 65,536 code positions

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Unicode 16-bit character set; 65,536 code

positions not sufficient for all the characters

required for Chinese, Japanese, and Korean scripts in distinct positions CJK-consolidation: characters of these

scripts are given the same value if they look the same

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Unicode Code values for all the characters used

to write contemporary ’major’ languages also the classical forms of some languages Latin, Greek, Cyrillic, Armenian, Hebrew,

Arabic, Devanagari, Bengali, Gurmukhi, Gujarati, Oriya, Tamil, Telugu, Kannada, Malayalam, Thai, Lao, Georgian, Tibetan

Chinese, Japanese, and Korean ideograms, and the Japanese and Korean phonetic and syllabic scripts

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Unicode punctuation marks technical and mathematical symbols arrows dingbats (pointing hands, stars, …) both accented letters and separate diacritical

marks (accents, tildes…) are included, with a mechanism for building composite characters

can also create problems: two characters that look the same may have different code values

->normalization may be necessary

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Unicode Code values for nearly 39,000 symbols

are provided some part is reserved for an expansion

method (see later) 6,400 code points are reserved for

private use they will never be assigned to any character

by the standard, so they will not conflict with the standard

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Unicode: encodings the ”native” Unicode encoding is

UCS-2 presents each code number as two

consecutive octets m and n code number = 256m + n (=2-byte integer)

can be inefficient for text containing ISO Latin characters only,

the length of the Unicode encoded sequence is twice the length of the ISO 8859-1 encoding

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Unicode: encodings UTF-8

ASCII code values are likely to be more common in most text than any other values

in UTF-8 encoding, ASCII characters are sent themselves (high-order bit 0)

other characters (two bytes) are encoded using 2-6 bytes (high-order bit is set to 1)

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Unicode: encodings UTF-16: expansion method

two 16-bit values are combined to a 32-bit value -> a million characters available

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Use of character codes try to use character codes logically

don’t choose a character just because it looks right

inform applications of the encoding used MIME headers, XML/HTML document

declarations Should be the responsibility of the

authoring applications… but…

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3. Text summarization ”Process of distilling the most

important information from a source to produce an abridged version for a particular user or task”

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Text summarization Many everyday uses:

headlines (from around the world) outlines (notes for students) minutes (of a meeting) reviews (of books, movies) ...

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Architecture of a text summarization system Input:

a single document or multiple documents

text, images, audio, video database

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Architecture of a text summarization system output:

extract or abstract compression rate

ratio of summary length to source length connected text or fragmentary generic or user-focused/domain-

specific indicative or informative

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Architecture of a text summarization system Three phases:

analyzing the input text transforming it into a summary

representation synthesizing an appropriate output

form

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Condensation operations Selection of more salient

(=”keskeinen”, ”essential”) or non-redundant information

aggregation of information (e.g. from different parts of the source, or of different linguistic descriptions)

generalization of specific information with more general, abstract information

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The level of processing surface level discourse level

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Surface-level approaches Tend to represent information in

terms of shallow features the features are then selectively

combined together to yield a salience function used to extract information

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Surface level Shallow features

thematic features presence of statistically salient terms, based on

term frequency statistics location

position in text, position in paragraph, section depth, particular sections

background presence of terms from the title or headings in

the text, or from the user’s query

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Surface level Cue words and phrases

”in summary”, ”our investigation” emphasizers like ”important”, ”in

particular” domain-specific bonus (+ ) and stigma (-)

terms

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Discourse-level approaches Model the global structure of the text

and its relation to communicative goals

structure can include: format of the document (e.g. hypertext

markup) threads of topics as they are revealed in

the text rhetorical structure of the text, such as

argumentation or narrative structure

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Classical approaches Luhn ’58 Edmundson ’69

general idea: give a score to each sentence choose the sentences with the

highest score to be included in the summary

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Luhn’s method Filter terms in the document using a stoplist Terms are normalized based on combining

together ortographically similar terms differentiate, different, differently, difference -> differen

Frequencies of combined terms are calculated and non-frequent terms are removed

-> ”significant” terms remain

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Luhn’s method Sentences are weighted using the

resulting set of ”significant” terms and a term density measure: each sentence is divided into segments

bracketed by significant terms not more than 4 non-significant terms apart

each segment is scored by taking the square of the number of bracketed significant terms divided by the total number of bracketed terms

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Exercise (CNN News) Let {13, computer, servers,

Internet, traffic, attack, officials, said} be significant words.

”Nine of the 13 computer servers that manage global Internet traffic were crippled by a powerful electronic attack this week, officials said.”

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Exercise (CNN News) Let {13, computer, servers,

Internet, traffic, attack, officials, said} be significant words.

* * * [13 computer servers * * * Internet traffic] * * * * * * [attack * * officials said]

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Exercise (CNN News) [13 computer servers * * * Internet

traffic] score: 52 / 8 = 25/8 = 3.1

[attack * * officials said] score: 32 / 5 = 9/5 = 1.8

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Luhn’s method the score of the highest scoring

segment is taken as the sentence score

the highest scoring sentences are chosen to the summary a cutoff value is given

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”Modern” application text summarization of web pages

on handheld devices (Buyukkokten, Garcia-Molina, Paepcke; 2001)

macro-level summarization micro-level summarization

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Web page summarization macro-level summarization

The web page is partitioned into ‘Semantic Textual Units’ (STUs)

Paragraphs, lists, alt texts (for images) Hierarchy of STUs is identified

List - list item, table – table row Nested STUs are hidden

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Web page summarization micro-level summarization: 5

methods tested for displaying STUs in several states incremental: 1) the first line, 2) the

first three lines, 3) the whole STU all: the whole STU in a single state keywords: 1) important keywords, 2)

the first three lines, 3) the whole STU

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Web page summarization summary: 1) the STUs ’most significant’

sentence is displayed, 2) the whole STU keyword/summary: 1) keywords, 2) the

STUs ’most significant’ sentence, 3) the whole STU

The combination of keywords and a summary has given the best performance for discovery tasks on web pages

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Web page summarization extracting summary sentences

Sentences are scored using a variant of Luhn’s method:

Words are TF*IDF weighted; given a weight cutoff value, the high scoring words are selected to be significant words

Weight of a segment: sum of the weights of significant words divided by the total number of words within a segment

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Edmundson’s method Extends earlier work to look at

three features in addition to word frequencies: cue phrases (e.g. ”significant”,

”impossible”, ”hardly”) title and heading words location

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Edmundson’s method Programs to weight sentences based on

each of the four features weight of a sentence = the sum of the

weights for features programs were evaluated by comparison

against manually created extracts corpus-based methodology: training set

and test set in the training phase, weights were manually

readjusted

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Edmundson’s method Results:

three additional features dominated word frequency measures

the combination of cue-title-location was the best, with location being the best individual feature

keywords alone was the worst

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Fundamental issues What are the most powerful but

also more general features to exploit for summarization?

How do we combine these features?

How can we evaluate how well we are doing?

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Corpus-based approaches In the classical methods, various

features (thematic features, title, location, cue phrase) were used to determine the salience of information for summarization

an obvious issue: determine the relative contribution of different features to any given text summarization task

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Corpus-based approaches Contribution is dependent on the

text genre, e.g. location: in newspaper stories, the leading text

often contains a summary in TV news, a preview segment may

contain a summary of the news to come

in scientific text: an author-written abstract

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Corpus-based approaches The importance of different text

features for any given summarization problem can be determined by counting the occurrences of such features in text corpora

in particular, analysis of human-generated summaries, along with their full-text sources, can be used to learn rules for summarization

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Corpus-based approaches Challenges

creating a suitable text corpus, designing an annotation scheme

ensuring the suitable set of summaries is available

may already be available: scientific papers

if not: author, professional abstractor, judge

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KPC method Kupiec, Pedersen, Chen (1995): A

Trainable Document Summarizer a learning method using a corpus

of abstracts written by professional human abstractors (Engineering Information Co.)

naïve Bayesian classification method is used

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KPC method: general idea training phase:

Select a set of features Calculate a probability of each feature

value to appear in a summary sentence

using a training corpus (e.g. originals + manual summaries)

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KPC method: general idea when a new document is

summarized: For each sentence

Find values for the features Calculate the probability for this feature

value combination to appear in a summary sentence

Choose n best scoring sentences

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KPC method: features

sentence-length cut-off feature given a threshold (e.g. 5 words), the

feature is true for all sentences longer than the threshold, and false otherwise

F1(s) = 0, if sentence s has 5 or less words F1(s) = 1, if sentence s has more than 5

words

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KPC method: features paragraph feature

sentences in the first 10 paragraphs and the last 5 paragraphs in a document get a higher value

in paragraphs: paragraph-initial, paragraph-final, paragraph-medial are distinguished

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KPC method: features paragraph feature

F2(s) = i, if sentence s is the first sentence in a paragraph

F2(s) = f, if there are at least 2 sentences in a paragraph, and s is the last one

F2(s) = m, if there are at least 3 sentences in a paragraph, and is neither the first nor the last sentence

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KPC method: features thematic word feature

a small number of thematic words (the most frequent content words) are selected

each sentence is scored as a function of frequency of the thematic words

highest scoring sentences are selected binary feature: feature is true for a

sentence, if the sentence is present in the set of highest scoring sentences

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KPC method: features fixed-phrase feature

this feature is true for sentences that contain any of 26 indicator phrases (e.g. ”this letter…”, ”In conclusion…”), or that follow section head that contain specific keywords (e.g. ”results”, ”conclusion”)

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KPC method: features Uppercase word feature

proper names and explanatory text for acronyms are usually important

feature is computed like the thematic word feature

an uppercase thematic word is not sentence-initial and begins with a capital

letter and must occur several times first occurrence is scored twice as much as

later occurrences

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Exercise (CNN news) sentence-length; F1: let threshold = 14

< 14 words: F1(s) =0, else F1(s)=1 paragraph; F2:

i=first, f=last, m=medial thematic-words; F3

score: how many thematic words a sentence has

F3(s) = 0, if score > 3, else F3(s) = 1

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KPC method: classifier For each sentence s, we compute

the probability that s will be included in a summary S given the k features Fj, j=1…k

the probability can be expressed using Bayes’ rule:

),...,(

)()|,...(),...,|(

1

11

k

kk

FFP

SsPSsFFPFFSsP

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KPC method: classifier Assuming statistical independence of

the features:

P(sS) is a constant, and P(Fj| sS) and P(Fj) can be estimated directly from the training set by counting occurrences

)(

)()|(),...|(

1

11

k

jj

k

jj

k

FP

SsPSsFPFFSsP

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KPC method: corpus Corpus is acquired from

Engineering Information Co, which provides abstracts of technical articles to online information services

articles do not have author-written abstracts

abstracts were created by professional abstractors

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KPC method: corpus 188 document/summary pairs sampled

from 21 publications in the scientific/technical domain

summaries are mainly indicative, average length is 3 sentences

average number of sentences in the original documents is 86

author, address, and bibliography were removed

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KPC method: sentence matching The abstracts from the human

abstractors are not extracts but inspired by the original sentences

the automatic summarization task here: extract sentences that the human

abstractor might have chosen to prepare summary text (with minor modifications…)

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KPC method: sentence matching For training, a correspondence

between the manual summary sentences and sentences in the original document need to be obtained

matching can be done in several ways

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KPC method: sentence matching matching can be done in several

ways: a direct sentence match

the same sentence is found in both a direct join

2 or more original sentences were used to form a summary sentence

summary sentence can be ’unmatchable’ summary sentence (single or joined) can

be ’incomplete’

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KPC method: sentence matching Matching was done in two passes

first, the best one-to-one sentence matches were found automatically

second, these matches were used as a starting point for the manual assignment of correspondences

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KPC method: evaluation Cross-validation strategy for

evaluation documents from a given journal were

selected for testing one at a time; all other document/summary pairs were used for training

unmatchable and incomplete summary sentences were excluded

total of 498 unique sentences

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KPC method: evaluation Two ways of evaluation

1. the fraction of manual summary sentences that were faithfully reproduced by the summarizer program the summarizer produced the same number of

sentences as were in the corresponding manual summary

-> 35% of summary sentences reproduced 83% is the highest possible value, since unmatchable

and incomplete sentences were excluded

2. the fraction of the matchable sentences that were correctly identified by the summarizer -> 42%

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KPC method: evaluation

the effect of different features was also studied

best combination (44%): paragraph, fixed-phrase, sentence-length

baseline: selecting sentences from the beginning of the document (result: 24%)

if 25% of the original sentences selected: 84%

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Discourse-based approaches Discourse structure appears to play an

important role in the strategies used by human abstractors and in the structure of their abstracts

an abstract is not just a collection of sentences, but it has an internal structure -> abstract should be coherent and it should

represent some of the argumentation used in the source

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Discourse models

cohesion relations between words or referring

expressions, which determine how tightly connected the text is

anaphora, ellipsis, synonymy, hypernymy (dog is-a-kind animal)

coherence overall structure of a multi-sentence text in

terms of macro-level relations between sentences (e.g. ”although” -> contrast)

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Boguraev, Kennedy (BG) Goal: identify those phrasal units

across the entire span of the document that best function as representative highlights of the document’s content

these phrasal units are called topic stamps

a set of topic stamps is called capsule overview

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BG A capsule overview

not a set/sequence of sentences a semi-formal (normalised)

representaion of the document, derived after a process of data reduction over the original text

not always very readable, but still represents the flow of the narrative

can be combined with surrounding information to produce more coherent presentation

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Priest is charged with Pope attack

A Spanish priest was charged here today with attempting to murder the Pope. Juan Fernandez Krohn, aged 32, was arrested after a man armed with a bayonet approached the Pope while he was saying prayers at Fatima on Wednesday night.

According to the police, Fernandez told the investigators today that he trained for the past six months for the assault. He was alleged to have claimed the Pope ’looked furious’ on hearing the priest’s criticism of his handling of the church’s affairs. If found quilty, the Spaniard faces a prison sentence of 15-20 years.

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Capsule overview vs. summary summary could be, e.g.

“A Spanish priest is charged after an unsuccessful murder attempt on the Pope”

capsule overview: A SPANISH PRIEST was charged Attempting to murder the POPE HE trained for the assault POPE furious on hearing PRIEST’S criticisms

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BG Primary consideration: methods

should apply to any document type and source (domain independence)

also: efficient and scalable technology shallow syntactic analysis, no

comprehensive parsing engine needed

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BG

Based on the findings on technical terms technical terms have such linguistic properties

that can be used to find terms automatically in different domains quite reliably

technical terms seem to be topical task of content characterization

identifying phrasal units that have lexico-syntactic properties similar to technical terms discourse properties that signify their status as most

prominent

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BG: terms as content indicators Problems

undergeneration overgeneration differentiation

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Undergeneration a set of phrases should contain an

exhaustive description of all the entities that are discussed in the text

the set of technical terms has to be extended to include also expressions with pronouns etc.

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Overgeneration already the set of technical terms

can be large extensions make the information

overload even worse solution: phrases that refer to one

participant in the discourse are combined with referential links

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Differentiation The same list of terms may be used

to describe two documents, even if they, e.g., focus on different subtopics

it is necessary to differentiate term sets not only according to their membership, but also according to the relative representativeness of the terms they contain

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Term sets and coreference classes Phrases are extracted using a

phrasal grammar (e.g. a noun with modifiers) also expressions with pronouns and

incomplete expressions are extracted using a (Lingsoft) tagger that provides

information about the part of speech, number, gender, and grammatical function of tokens in a text

solves the undergeneration problem

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Term sets and coreference classes The phrase set has to be reduced

to solve the problem of overgeneration

-> a smaller set of expressions that uniquely identify the objects referred to in the text

application of anaphora resolution e.g. to which noun a pronoun ’he’

refers to?

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Resolving coreferences Procedure

moving through the text sentence by sentence and analysing the nominal expressions in each sentence from left to right

either an expression is identified as a new participant in the discourse, or it is taken to refer to a previously mentioned referent

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Resolving coreferences Coreference is determined by a 3 step

procedure a set of candidates is collected: all nominals

within a local segment of discourse some candidates are eliminated due to

morphological mismatches or syntactical restrictions

remaining candidates are ranked according to their relative salience in the discourse

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Salience factors sent(term) = 100 iff term is in the

current sentence cntx(term) = 50 iff term is in the

current discourse segment subj(term) = 80 iff term is a subject acc(term) = 50 iff term is a direct object dat(term) = 40 iff term is an indirect obj ...

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Local salience of a candidate The local salience of a candidate is the

sum of the values of the salience factors the most salient candidate is selected

as the antecedent if the coreference link cannot be

established to some other expression, the nominal is taken to introduce a new referent

-> coreferent classes

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Topic stamps In order to further reduce the

referent set, some additional structure has to be imposed the term set is ranked according to the

salience of its members relative prominence or importance in

the discourse of the entities to which they refer

objects in the centre of discussion have a high degree of salience

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Saliency Measured like local saliency in

coreference resolution, but tries to measure the importance of unique referents in the discourse

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Priest is charged with Pope attack

A Spanish priest was charged here today with attempting to murder the Pope. Juan Fernandez Krohn, aged 32, was arrested after a man armed with a bayonet approached the Pope while he was saying prayers at Fatima on Wednesday night.

According to the police, Fernandez told the investigators today that he trained for the past six months for the assault. He was alleged to have claimed the Pope ’looked furious’ on hearing the priest’s criticism of his handling of the church’s affairs. If found quilty, the Spaniard faces a prison sentence of 15-20 years.

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Saliency

’priest’ is the primary element eight references to the same actor in the body

of the story these reference occur in important syntactic

positions: 5 are subjects of main clauses, 2 are subjects of embedded clauses, 1 is a possessive

’Pope attack’ is also important ’Pope’ occurs 5 times, but not in so important

positions (2 are direct objects)

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Discourse segments If the intention is to use very concise

descriptions of one or two salient phrases, i.e. topic stamps, longer text have to be broken down into smaller segments

topically coherent, contiguous segments can be found by using a lexical similarity measure assumption: distribution of words used

changes when the topic changes

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BG: Summarization process

1. linguistic analysis2. discourse segmentation3. extended phrase analysis4. anaphora resolution5. calculation of discourse salience6. topic stamp identification7. capsule overview

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Knowledge-rich approaches Structured information can be used as

the starting point for summarization structured information: e.g. data and

knowledge bases, may have been produced by processing input text

summarizer does not have to address the linguistic complexities and variability of the input, but also the structure of the input text is not available

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Knowledge-rich approaches There is a need for measures of

salience and relevance that are dependent on the knowledge source

addressing coherence, cohesion, and fluency becomes the entire responsibility of the generator

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STREAK McKeown, Robin, Kukich (1995):

Generating concise natural language summaries

goal: folding information from multiple facts into a single sentence using concise linguistic constructions

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STREAK Produces summaries of basketball

games first creates a draft of essential

facts then uses revision rules

constrained by the draft wording to add in additional facts as the text allows

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STREAK Input:

a set of box scores for a basketball game historical information (from a database)

task: summarize the highlights of the game,

underscoring their significance in the light of previous games

output: a short summary: a few sentences

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STREAK The box score input is represented as

a conceptual network that expresses relations between what were the columns and rows of the table

essential facts: the game result, its location, date and at least one final game statistic (the most remarkable statistic of a winning team player)

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STREAK Essential facts can be obtained

directly from the box-score in addition, other potential facts

other notable game statistics of individual players - from box-score

game result streaks (Utah recorded its fourth straight win) - historical

extremum performances such as maximums or minimums - historical

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STREAK Essential facts are always included potential facts are included if there

is space decision on the potential facts to be

included could be based on the possibility to combine the facts to the essential information in cohesive and stylistically successful ways

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STREAK Given facts:

Karl Malone scored 39 points. Karl Malone’s 39 point performance is

equal to his season high a single sentence is produced:

Karl Malone tied his season high with 39 points

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Text summarization surface-level methods

“manual” features corpus-based learning

discourse-level methods knowledge-rich methods