Event-Centric Summary Generation Lucy Vanderwende, Michele Banko and Arul Menezes One Microsoft Way, WA, USA DUC 2004

2 Abstract Our primary interest is two folds: –To explore an event-centric approach to summarization –To explore a generation approach to summary realization

3 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

4 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

5 Creating document representations Cluster sentence Analysis sentence and get logical form

6 Creating document representations Produces triples result from logical form –(LFNode i, rel, LFNode j )

7 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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9 Node scoring Using Pagerank Using Pagerank algorithm –Hyperlink such as WWW –When link between nodes, vote for that node –

10 Node scoring Using Pagerank Pagerank framework –“Pages”, correspond to base forms of words in the documents –“hyperlink”, correspond to semantic relationships –Verbs, identify events –Noun, Identify entities –Use event to identify summary content Typically, the algorithm converges around 40 iterations

11 Graph Scoring Use pagerank scores to assess the link weight (LW(i->n))

12 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 same entity within the selected event fragment

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14 Summary Generation Event fragment order –Cluster event fragment by they refer to –Choose the greatest number of argument node for the event –Order the selected event fragments To group sentence referring to the same entity together Order sentence which exhibit event-coreference

15 Experiments and Evaluation (Rule-based pronoun resolution method, 75% accuracy)

16 Experiments and Evaluation Reason: the potential to introduce disfluent text

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18 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