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Abstract

Automatic document summarization using citations is based on summarizing what others explicitly say about the document, by extracting a summary from text around the citations (citances). While this technique works quite well for summarizing the impact of scientific articles, other genres of documents as well as other types of summaries require different approaches. In this paper, we introduce a new family of methods that we developed for legal documents summarization to generate catchphrases for legal cases (where catchphrases are a form of legal summary). Our methods use both incoming and outgoing citations, and we show how citances can be combined with other elements of cited and citing documents, including the full text of the target document, and catchphrases of cited and citing cases. On a legal summarization corpus, our methods outperform competitive baselines. The combination of full text sentences and catchphrases from cited and citing cases is particularly successful. We also apply and evaluate the methods on scientific paper summarization, where they perform at the level of state-of-the-art techniques. Our family of citation-based summarization methods is powerful and flexible enough to target successfully a range of different domains and summarization tasks.

Type

a

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Abstract

Knowledge-based systems (KBS) are not necessarily based on a well-defined ontologies. In particular it is possible to build very successful KBS for classification problems, but where the classes or conclusions are entered by experts as free-text sentences with little constraint on textual consistency and little systematic organisation of the conclusions. This paper investigates how relations between such 'classes' may be discovered from existing knowledge bases. We have based our approach on KBS built with Ripple Down Rules (RDR). RDR is a knowledge acquisition and knowledge maintenance method which allows KBS to be built very rapidly and simply by correcting errors, but does not require a strong ontology. Our experimental results are based on a large real-world medical RDR KBS. The motivation for our work is to allow an ontology in a KBS to 'emerge' during development, rather than requiring the ontology to be established prior to the development of the KBS. It follows earlier work on using Formal Concept Analysis (FCA) to discover ontologies in RDR KBS

Type

a