Summarization of legal texts with high cohesion and automatic compression rate
JSAI-isAI'12 Proceedings of the 2012 international conference on New Frontiers in Artificial Intelligence
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The main problem for generating an extractive automatic text summary is to detect the most relevant information in the source document. For such purpose, recently some approaches have successfully employed the word sequence information from the self-text for detecting the candidate text fragments for composing the summary. In this paper, we employ the so-called n-grams and maximal frequent word sequences as features in a vector space model in order to determine the advantages and disadvantages for extractive text summarization.