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Biased LexRank : Passage Retrieval using Random Walks with Question-Based Priors

Biased LexRank : Passage Retrieval using Random Walks with Question-Based Priors. Presenter : JHOU, YU-LIANG Authors : Jahna Otterbacher a , Gunes Erkan b , Dragomir R. Radev 2009, IPM. Outlines. Motivation Objectives Methodology Experimental Result Conclusions Comments.

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Biased LexRank : Passage Retrieval using Random Walks with Question-Based Priors

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  1. Biased LexRank: Passage Retrieval using Random Walks with Question-Based Priors Presenter : JHOU, YU-LIANGAuthors : JahnaOtterbacher a , GunesErkan b , Dragomir R. Radev2009, IPM

  2. Outlines • Motivation • Objectives • Methodology • Experimental Result • Conclusions • Comments

  3. 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.

  4. Objectives Using biased LexRankon achieving text summarization and retrieval QA more effect.

  5. LexRank

  6. Biased LexRank

  7. Biased LexRank-application-QA QA systems is to retrieve the sentences that potentially contain the answer to the question .

  8. Passage retrieval-summarization Computing link weights

  9. Passage retrieval- Question answering

  10. Experimental-result biased LexRankv.s human summarizers

  11. Experimental-QA corpus

  12. Experimental Result effect of similarity for QA

  13. Experimental Result LexRank Versus the Baseline Approach

  14. Experimental Result LexRank Versus the Baseline Approach

  15. 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.

  16. Comments I think the method improved retrieval performance and comparable to human summarizers. Applications - Text summarization - Information retrieval

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