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In paper, we have proposed a novel summarization framework to generate a quality summary by extracting Relevant-Informative-Novel (RIN) sentences from topically related document collection called as RIN-Sum. In the proposed framework, with the aim to retrieve user's relevant informative sentences conveying novel information, ranking of structured sentences has been carried out. For sentence ranking, Relevant-Informative-Novelty (RIN) ranking function is formulated in which three factors, i.e., the relevance of sentence with input query, informativeness of the sentence and the novelty of the sentence have been considered. For relevance measure instead of incorporating existing metrics, i.e., Cosine and Overlap which have certain limitations, a new relevant metric called as C-Overlap has been formulated. RIN ranking is applied on document collection to retrieve relevant sentences conveying significant and novel information about the query. These retrieved sentences are used to generate query-specific summary of multiple documents. The performance of proposed framework have been investigated using standard dataset, i.e., DUC2007 documents collection and summary evaluation tool, i.e., ROUGE.

RIN-Sum: A System for Query-Specific Multi-Document Extractive Summarization