Extractive Summarization using Inter- and Intra- Event Relevance

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 369–376, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Extractive Summarization using Inter- and Intra- Event Relevance Wenjie Li, Mingli Wu and Qin Lu Department of Computing The Hong Kong Polytechnic University {cswjli,csmlwu,csluqin}@comp .polyu.edu.hk Wei Xu and Chunfa Yuan Department of Computer Science and Technology, Tsinghua University {vivian00,cfyuan}@mail.ts inghua.edu.cn Abstract Event-based summarization attempts to select and organize the sentences in a summary with respect to the events or the sub-events that the sentences de- scribe. Each event has its own internal structure, and meanwhile often relates to other events semantically, temporally, spatially, causally or conditionally. In this paper, we define an event as one or more event terms along with the named entities associated, and present a novel approach to derive intra- and inter- event relevance using the information of inter- nal association, semantic relatedness, distributional similarity and named en- tity clustering. We then apply PageRank ranking algorithm to estimate the sig- nificance of an event for inclusion in a summary from the event relevance de- rived. Experiments on the DUC 2001 test data shows that the relevance of the named entities involved in events achieves better result when their rele- vance is derived from the event terms they associate. It also reveals that the topic-specific relevance from documents themselves outperforms the semantic relevance from a general purpose knowledge base like Word-Net. 1. Introduction Extractive summarization selects sentences which contain the most salient concepts in documents. Two important issues with it are how the concepts are defined and what criteria should be used to judge the salience of the con- cepts. Existing work has typically been based on techniques that extract key textual elements, such as keywords (also known as significant terms) as weighed by their tf*idf score, or con- cepts (such as events or entities) with linguistic and/or statistical analysis. Then, sentences are selected according to either the important textual units they contain or certain types of inter- sentence relations they hold. Event-based summarization which has e- merged recently attempts to select and organize sentences in a summary with respect to events or sub-events that the sentences describe. With re- gard to the concept of events, people do not have the same definition when introducing it in different domains. While traditional linguistics work on semantic theory of events and the se- mantic structures of verbs, studies in information retrieval (IR) within topic detection and tracking framework look at events as narrowly defined topics which can be categorized or clustered as a set of related documents (TDT). IR events are broader (or to say complex) events in the sense that they may include happenings and their causes, consequences or even more extended effects. In the information extraction (IE) community, events are defined as the pre-specified and struc- tured templates that relate an action to its participants, times, locations and other entities involved (MUC-7). IE defines what people call atomic events. Regardless of their …

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