Abstract
Document summarization can be viewed as a reductive distilling of source text through content condensation, while words with high quantities of information are believed to carry more content and thereby importance. In this paper, we propose a new quantification measure for word significance used in natural language processing (NLP) tasks, and successfully apply it to an extractive text summarization approach. In a query-based summarization setting, the correlation between user queries and sentences to be scored is established from both the micro (i.e. at the word level) and the macro (i.e. at the sentence level) perspectives, resulting in an effective ranking formula. The experiments, both on a generic single document summarization evaluation, and on a query-based multi-document evaluation, verify the effectiveness of the proposed measures and show that the proposed approach achieves a state-of-the-art performance.
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Liu, X., Webster, J.J., Kit, C. (2009). An Extractive Text Summarizer Based on Significant Words. In: Li, W., Mollá-Aliod, D. (eds) Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy. ICCPOL 2009. Lecture Notes in Computer Science(), vol 5459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00831-3_16
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DOI: https://doi.org/10.1007/978-3-642-00831-3_16
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