Abstract
We propose a text summarization system known as MySum in finding the significance of sentences in order to produce a summary based on asymmetric word similarity and topic similarity. We use mass assignment theory to compute similarity between words based on the basis of their contexts. The algorithm is incremental so that words or documents can be added or subtracted without massive re-computation. Words are considered similar if they appear in similar contexts, however, these words do not have to be synonyms. We also compute the similarity of a sentence to the topic using frequency of overlapping words. We compare the summaries produced with the ones by humans and other system known as TF.ISF (term frequency-inverse sentence frequency). Our method generates summaries that are up to 60% similar to the manually created summaries taken from DUC 2002 test collection.
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Azmi-Murad, M., Martin, T. (2006). Sentence Extraction Using Asymmetric Word Similarity and Topic Similarity. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_39
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DOI: https://doi.org/10.1007/3-540-31662-0_39
Publisher Name: Springer, Berlin, Heidelberg
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