biased lexrank : passage retrieval using random walks with question-based priors

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Presenter : JHOU, YU-LIANG

Authors : Jahna Otterbacher a , Gunes Erkan b , Dragomir R. Radev

2009, IPM

Biased LexRank: Passage Retrieval usingRandom Walks with Question-Based Priors

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Outlines

MotivationObjectivesMethodology Experimental ResultConclusionsComments

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Motivation• Text summarization is one of the hardest problems in

information retrieval, because it is not very well-defined.

• There are various definitions of text summarization resulting from different approaches to solving the problem.

• There is often no agreement as to what a good summary is even when we are dealing with a particular definition of the problem.

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ObjectivesUsing biased LexRank on achieving text summarization and retrieval QA more effect .

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LexRank

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Biased LexRank

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Biased LexRank-application-QA

QA systems is to retrieve the sentences that potentially contain the answer to the question .

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Passage retrieval-summarization

Computing link weights

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Passage retrieval- Question answering

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Experimental-result

biased LexRank v.s human summarizers

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Experimental-QAcorpus

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Experimental Result effect of similarity for QA

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Experimental Result LexRank Versus the Baseline Approach

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Experimental ResultLexRank Versus the Baseline Approach

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Conclusions

In the paper, we have also demonstrated the

effectiveness of our method as applied to two classical

IR problems, extractive text summarization and passage

retrieval for question answering.

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CommentsI think the method improved retrieval performance and comparable to human summarizers.Applications

- Text summarization- Information retrieval