We argue that the advent of large volumes of full-length text, as opposed to short texts like abstracts and newswire, should be accompanied by corresponding new approaches to information access. Toward this end, we discuss the merits of imposing structure on full- length text documents; that is, a partition of the text into coherent multi-paragraph units that represent the pattern of sub- topics that comprise the text. Using this structure, we can make a distinction between the main topics, which occur throughout the length of the text, and the subtopics, which are of only limited extent. We discuss why recognition of subtopic structure is important and how, to some degree of accuracy, it can be found. We describe a new way of specifying queries on full-length documents and then describe an experiment in which making use of the recognition of local structure achieves better results on a typical information retrieval task than does a standard 1R measure.
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Subtopic structuring for full-length document access
SIGIR '93: Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrievalWe argue that the advent of large volumes of full-length text, as opposed to short texts like abstracts and newswire, should be accompanied by corresponding new approaches to information access. Toward this end, we discuss the merits of imposing ...
Subtopic-Focused Sentence Scoring in Multi-document Summarization
ALPIT '07: Proceedings of the Sixth International Conference on Advanced Language Processing and Web Information Technology (ALPIT 2007)In previous works, subtopics are seldom mentioned in multi-document summarization while only one topic is focused to extract summary. In this paper, we propose a subtopic- focused model to score sentences in the extractive summarization task. Different ...
Subtopic-based Multi-documents Summarization
CSO '10: Proceedings of the 2010 Third International Joint Conference on Computational Science and Optimization - Volume 02Multi-documents summarization is an important research area of NLP. Most methods or techniques of multidocument summarization either consider the documents collection as single-topic or treat every sentence as single-topic only, but lack of a systematic ...