Cluster Priority Based Sentence Ranking for Efficient Extractive Text Summaries
نویسندگان
چکیده
This paper presents a cluster priority ranking based approach for extractive automatic text summarization that aggregates different cluster ranks for final sentence scoring. This approach does not require any learning, feature weighting and semantic processing. Surface level features combinations are used for individual cluster scoring. Proposed approach produces quality summaries without using title feature. Experimental results on DUC 2002 dataset proves robustness of proposed approach as compared to other surface level approaches using ROUGE evaluation matrices.
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تاریخ انتشار 2015