Accurate user directed summarization from existing tools

This paper describes a set of experimental results produced from the TIPSTER SUMMAC initiative on user directed summaries: document summaries generated in the context of an information need expressed as a query. The summarizer that was evaluated was based on a set of existing statistical techniques that had been applied successfully to the INQUERY retrieval system. The techniques proved to have a wider utility, however, as the summarizer was one of the better performing systems in the SUMMAC evaluation. The design of this summarizer is presented with a range of evaluations: both those provided by SUMMAC as well as a set of preliminary, more informal, evaluations that examined additional aspects of the summaries. Amongst other conclusions, the results reveal that users can judge the relevance of documents from their summary almost as accurately as if they had had access to the document’s full text

Generating indicative-informative summaries with SumUM

We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies