Multi-Document Summarization
Query-based Summarization
(Sprache: Englisch)
The world is overloaded with information due to the internet revolution. This calls for an efficient and accurate summarization system to extract relevant information. Text summarization system automatically generates a summary of a given document and helps...
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The world is overloaded with information due to the internet revolution. This calls for an efficient and accurate summarization system to extract relevant information. Text summarization system automatically generates a summary of a given document and helps people to take effective decisions in less time. In this book two methods have been proposed for query-focused multi-document summarization that uses k-mean clustering and term-frequency-inverse-sentence-frequency method for sentence weighting to rank the sentences of the document(s) with respect to a given query. The proposed methods find the proximity of documents and query, and later uses this proximity to rank sentences of each document. A comparative study for proposed methods has been done and experimental results shows that both methods are comparable because of a slight difference in performance. DUC 2007 test dataset and ROUGH-1.5.5 summarization evaluation package is used for evaluation purpose.
Bibliographische Angaben
- Autor: Rohit Pachlor
- 2013, 100 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 3659453226
- ISBN-13: 9783659453229
Sprache:
Englisch
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