Event-Centric Summary Generation

Lucy Vanderwende, Michele Banko and Arul Menezes

One Microsoft Way, WA, USA DUC 2004

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Abstract

• Our primary interest is two folds:– To explore an event-centric approach to

summarization– To explore a generation approach to summary

realization

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Introduction

• Identifying important events, as opposed to entities

• Generation component– Human-authored rely less on sentence

extraction

• Graph-scoring algorithm– To identify highest weighted node to guide

content selection

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System Description

• MSR-NLP– Analysis component

• Rule-base syntactic analysis component• Produces a logical form

– Syntactic variations, words label

– Generation component• Syntactic realization component• Produces a syntactic tree

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Creating document representations

• Cluster sentence

• Analysis sentence and get logical form

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Creating document representations

• Produces triples result from logical form– (LFNodei, rel, LFNodej)

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Forming Document Graph

• Take those triples and join nodes by way of their semantic relation using a bidirectional link structure

• Keep track of how many times we observe the relationship

• Stop words are not included in the graph construction

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Node scoring Using Pagerank

• Using Pagerank algorithm– Hyperlink such as WWW– When link between nodes, vote for that node

–

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Node scoring Using Pagerank

• Pagerank framework– “Pages”, correspond to base forms of words in the do

cuments– “hyperlink”, correspond to semantic relationships– Verbs, identify events– Noun, Identify entities– Use event to identify summary content

• Typically, the algorithm converges around 40 iterations

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Graph Scoring

• Use pagerank scores to assess the link weight (LW(i->n))

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Summary Generation

• Generated by extracting and merging of logical form– Identify important triples

• Defined highly link weight node, and together with most highly weighted

• (leave, Tobj, LonLondon_Bridge_Hospital)• Not (leave, Tobj, government)

– Extract fragments divided into “event” and “entity”

• Event used to generate summary• Entity used to expanded upon reference to the sa

me entity within the selected event fragment

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Summary Generation

• Event fragment order– Cluster event fragment by they refer to – Choose the greatest number of argument nod

e for the event– Order the selected event fragments

• To group sentence referring to the same entity together

• Order sentence which exhibit event-coreference

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Experiments and Evaluation

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(Rule-based pronoun resolution method, 75% accuracy)

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Experiments and Evaluation

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Reason: the potential to introduce disfluent text

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Directions and Future Work

• Produce more human-like generated summaries

• Further study the impact of anaphora resolution

• Study new page-ranking algorithm• While ordering groups event fragments

mentioning the same entity, we have not yet implemented a system to combine them into larger logical form construction