The effectiveness of automatic text summarization in mobile learning contexts

Intelligent Database Systems Lab

Presenter: YU-TING LU

Authors: Guangbing Yang , Nian-Shing Chen , Kinshuk ,

Erkki Sutinen , Terry Anderson ,Dunwei Wen

2013. CE

The effectiveness of automatic text summarization in mobile learning contexts


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


Motivation• Reducing the amount of content transmitted

may negatively impact the meaning conveyed

within.

• Due to the problem of the oft-decried

information overload, delivering large amounts

of text contents makes mobile learners

challenging, especially for learning purposes.


Objectives• This study investigates automatic text summarization

to provide a tool set that reduces the quantity of

textual content for mobile learning support.

• This study aims to investigate a technology for

content processing that can be used to summarize

text contents effectively to align content size to

match various characteristics of mobile devices.


Methodology• Research

questions•Participants

•The system

•Experimental dataset

•Experimental task

•Experimental treatments


Methodology – Research questions

1. Identifies the general usefulness of the generated

summaries for learning purposes.

2. Determines what the optimal summaries will be if a

higher level of learning achievement is required.

3. Analyzes what kind of short summaries are still

helpful in reaching a sufficient level of learning.


Methodology – Participants

Participants-25Age 25-40

Background Non-IT staffs from a dot-com company in CanadaNative language English

Degrees At least high school graduatesDMbile device iPad2

Group1Gro

up2Group3Group4Group5

• 2 office clerks• 3 customer service representatives






Methodology – The system

M


Methodology – Experimental dataset & taskOriginal dataset (A traditional e-learning course module) 82 text-based reading modules

1430 words per module

Total of 119,640 words

2401 sentences

2671 unique words grouped as the vocabulary

Testing datasetFive modules

Range: 1308 to 1556 words

Total: 119,640 words

Three types of summaries: 　 Each generated summary had 100, 250, 400 words respectively


Methodology – Experimental dataset & task


Methodology – Experimental treatments


Experiments


Conclusions

• This summarization approach is able to generate

summaries effectively from learning contents.

• This study has the following limitations that could be

addressed in future research. – Sample size – Different backgrounds – Semantic differences or similarities


Comments• Advantages

- Generating summaries effectively

• Applications- Automatic text summarization- Mobile learning

The effectiveness of automatic text summarization in mobile learning contexts

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