Automatic Generation of Story Highlights

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 565–574, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Automatic Generation of Story Highlights Kristian Woodsend and Mirella Lapata School of Informatics, University of Edinburgh Edinburgh EH8 9AB, United Kingdom k.woodsend@ed.ac.uk, mlap@inf.ed.ac.uk Abstract In this paper we present a joint con- tent selection and compression model for single-document summarization. The model operates over a phrase-based rep- resentation of the source document which we obtain by merging information from PCFG parse trees and dependency graphs. Using an integer linear programming for- mulation, the model learns to select and combine phrases subject to length, cover- age and grammar constraints. We evalu- ate the approach on the task of generat- ing “story highlights”—a small number of brief, self-contained sentences that allow readers to quickly gather information on news stories. Experimental results show that the model’s output is comparable to human-written highlights in terms of both grammaticality and content. 1 Introduction Summarization is the process of condensing a source text into a shorter version while preserving its information content. Humans summarize on a daily basis and effortlessly, but producing high quality summaries automatically remains a chal- lenge. The difficulty lies primarily in the nature of the task which is complex, must satisfy many constraints (e.g., summary length, informative- ness, coherence, grammaticality) and ultimately requires wide-coverage text understanding. Since the latter is beyond the capabilities of current NLP technology, most work today focuses on extractive summarization, where a summary is created sim- ply by identifying and subsequently concatenating the most important sentences in a document. Without a great deal of linguistic analysis, it is possible to create summaries for a wide range of documents. Unfortunately, extracts are of- ten documents of low readability and text quality and contain much redundant information. This is in marked contrast with hand-written summaries which often combine several pieces of informa- tion from the original document (Jing, 2002) and exhibit many rewrite operations such as substitu- tions, insertions, deletions, or reorderings. Sentence compression is often regarded as a promising first step towards ameliorating some of the problems associated with extractive summa- rization. The task is commonly expressed as a word deletion problem. It involves creating a short grammatical summary of a single sentence, by re- moving elements that are considered extraneous, while retaining the most important information (Knight and Marcu, 2002). Interfacing extractive summarization with a sentence compression mod- ule could improve the conciseness of the gener- ated summaries and render them more informative (Jing, 2000; Lin, 2003; Zajic et al., 2007). Despite the bulk of work on sentence compres- sion and summarization (see Clarke and Lapata 2008 and Mani 2001 for overviews) only a handful of approaches attempt to do both in a joint model (Daum´e III and Marcu, 2002; Daum´e III, 2006; Lin, 2003; Martins and Smith, 2009). One rea- son for this might be the performance of sentence compression systems which falls short of attaining grammaticality levels of human output. For ex- …

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