Using syntactic information to extract relevant terms for multi-document summarization

Using Syntactic Information to Extract Relevant Terms for Multi-Document Summarization Enrique Amig´o Julio Gonzalo V´ıctor Peinado Anselmo Pe˜nas Felisa Verdejo Departamento de Lenguajes y Sistemas Inform´aticos Universidad Nacional de Educaci´on a Distancia c/ Juan del Rosal, 16 - 28040 Madrid - Spain http://nlp.uned.es Abstract The identification of the key concepts in a set of documents is a useful source of information for several information access applications. We are interested in its application to multi-document summarization, both for the automatic genera- tion of summaries and for interactive summa- rization systems. In this paper, we study whether the syntactic po- sition of terms in the texts can be used to predict which terms are good candidates as key con- cepts. Our experiments show that a) distance to the verb is highly correlated with the proba- bility of a term being part of a key concept; b) subject modifiers are the best syntactic locations to find relevant terms; and c) in the task of auto- matically finding key terms, the combination of statistical term weights with shallow syntactic information gives better results than statistical measures alone. 1 Introduction The fundamental question addressed in this article is: can syntactic information be used to find the key concepts of a set of documents? We will pro- vide empirical answers to this question in a multi- document summarization environment. The identification of key terms out of a set of doc- uments is a common problem in information access applications and, in particular, in text summariza- tion: a fragment containing one or more key con- cepts can be a good candidate to be part of a sum- mary. In single-document summarization, key terms are usually obtained from the document title or head- ing (Edmundson, 1969; Preston, 1994; Kupiec et al., 1995). In multi-document summarization, how- ever, some processing is needed to identify key con- cepts (Lin and Hovy, 2002; Kraaij et al., 2002; Schlesinger et al., 2002). Most approaches are based on statistical criteria. Criteria to elaborate a manual summary depend, by and large, on the user interpretation of both the information need and the content of documents. This is why this task has also been attempted from an interactive perspective (Boguraev et al., 1998; Buyukkokten et al., 1999; Neff and Cooper, 1999; Jones et al., 2002; Leuski et al., 2003). A standard feature of such interactive summarization assistants is that they offer a list of relevant terms (automati- cally extracted from the documents) which the user may select to decide or refine the focus of the sum- mary. Our hypothesis is that the key concepts of a doc- ument set will tend to appear in certain syntactic functions along the sentences and clauses of the texts. To confirm this hypothesis, we have used a test bed with manually produced summaries to study: • which are the most likely syntactic functions for the key concepts manually identified in the document sets. • whether this information can be used to auto- matically extract the relevant terms from a …

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