What have you read? based Multi-Document Summarization

Abstract

Due to the tremendous amount of data available today, extracting essential information from such a large volume of data is quite tough. Particularly in the case of text documents, which need a significant amount of time from the user to read the material and extract useful information. The major problem is identifying the user's relevant documents, removing the most significant pieces of information, determining document relevancy, excluding extraneous information, reducing details, and generating a compact, consistent report. For all these issues, we proposed a novel technique that solves the problem of extracting important information from a huge amount of text data and using previously read documents to generate summaries of new documents. Our technique is more focused on extracting topics (also known as topic signatures) from the previously read documents and then selecting the sentences that are more relevant to these topics based on update summary generation. Besides this, the concept of overlapping value is used that digs out the meaningful words and word similarities. Another thing that makes our work better is the Dice Coefficient which measures the intersection of words between document sets and helps to eliminate redundancy. The summary generated is based on more diverse and highly representative sentences with an average length. Empirically, we have observed that our proposed novel technique performed better with baseline competitors on the real-world TAC2008 dataset.