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Title:
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AUTOMATIC TEXT SUMMARIZATION USING
SEMANTICALLY EXTENDED WORD POSITION
INFORMATION |
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Author(s):
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Koji Samejima, Noriko Matsumoto and Norihiko Yoshida |
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ISBN:
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978-989-8533-85-2 |
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Editors:
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Piet Kommers, Pascal Ravesteijn, Guido Ongena and Pedro Isaías |
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Year:
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2019 |
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Edition:
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Single |
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Keywords:
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Extractive Summarization, Position and Semantic Information, WordNet |
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Type:
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Full Paper |
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First Page:
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105 |
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Last Page:
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111 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Many automatic text summarization services use extractive methods, which pick up sentences according to their
importance out of a given document. The importance is estimated using several metrics such as position information and
semantic information. Most methods combine these metrics using weighted linear summation. This paper proposes more
fine-grained integration of position information and semantic information to achieve better estimation of importance,
which leads to better quality of summaries. |
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