Deep Learning Architecture for Hybrid Multi-Document Abstractive Summarization using Sentence Embeddings - Kumari Singh, Anita; Shashi, M (International Journal of Computer Sciences and Engineering, 2020)
Multi-document summarization aims at generating a comprehensive summary of multiple documents related to a common topic without repeatedly conveying the same piece of information while covering the essential information from all the documents. Extractive summarization methods exist to handle Multi-document summarization, while the Abstractive summarization methods are limited to handling single-document summaries. This paper proposes abstractive summarization of multiple documents by extending the state-of-the-art single-document abstractive summarization model Pointer-Generator to generate a multi-document summary. The short abstract summaries generated upon multiple applications of the Pointer-Generator model on individual documents are clustered at the sentence level using Skip-thought embeddings. The representative sentences from each of the clusters constitute the final summary in order to avoid similar sentences while generating the multi-document abstractive summary without loss of information. The proposed methodology is evaluated using the DUC2004 benchmark dataset and observed a gain of 2 to 7 points of ROUGE scores compared to existing state of the art methods.