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

Intelligent Database Systems Lab

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


Outlines

MotivationObjectivesMethodology Experimental ResultConclusionsComments


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.


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


LexRank


Biased LexRank


Biased LexRank-application-QA

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


Passage retrieval-summarization

Computing link weights


Passage retrieval- Question answering


Experimental-result

biased LexRank v.s human summarizers


Experimental-QAcorpus


Experimental Result effect of similarity for QA


Experimental Result LexRank Versus the Baseline Approach


Experimental ResultLexRank Versus the Baseline Approach


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.


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

- Text summarization- Information retrieval

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

Documents

qa qa systems

retrieval qa

retrieval performance

biased lexrank v

ipm biased lexrank

extractive text summarization

hardest problems

classical ir problems