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.

Multi-Document Summarization, Abstractive, Skip-thought embeddings, ROUGE

[1]. Chen, CL Philip, and Chun-Yang Zhang. "Data-intensive applications, challenges, techniques and technologies: A survey on Big Data." Information sciences 275 (2014): 314-347.
[2]. Raghupathi, Wullianallur, and Viju Raghupathi. "Big data analytics in healthcare: promise and potential." Health information science and systems 2.1 (2014): 3.
[3]. Andreu-Perez, Javier, et al. "Big data for health." IEEE journal of biomedical and health informatics 19.4 (2015): 1193-1208.
[4]. Singh, Anita Kumari, and Mogalla Shashi. "Deep Learning Architecture for Multi-Document Summarization as a cascade of Abstractive and Extractive Summarization approaches." International Journal of Computer Sciences and Engineering 7.3 (2019): 950-954.
[5]. Kiros, Ryan, et al. "Skip-thought vectors." Advances in neural information processing systems. 2015.
[6]. Saggion, Horacio, and Thierry Poibeau. "Automatic text summarization: Past, present, and future." Multi-source, multilingual information extraction, and summarization. Springer, Berlin, Heidelberg, 2013. 3-21.
[7]. Chen, Kuan-Yu, et al. "Extractive broadcast News summarization leveraging recurrent neural network language modeling techniques." IEEE Transactions on Audio, Speech, and Language Processing 23.8 (2015): 1322-1334.
[8]. Cheng, Jianpeng, and Mirella Lapata. "Neural summarization by extracting sentences and words." arXiv preprint arXiv:1603.07252 (2016).
[9]. Verma, Sukriti, and Vagisha Nidhi. "Extractive summarization using deep learning." arXiv preprint arXiv:1708.04439 (2017).
[10]. Khan, Atif, and Naomie Salim. "A review of abstractive summarization methods." Journal of Theoretical and Applied Information Technology 59.1 (2014): 64-72.
[11]. Nallapati, Ramesh, et al. "Abstractive text summarization using sequence-to-sequence rnns and beyond." arXiv preprint arXiv:1602.06023 (2016).
[12]. Pasunuru, Ramakanth, Han Guo, and Mohit Bansal. "Towards improving abstractive summarization via entailment generation." Proceedings of the Workshop on New Frontiers in Summarization. 2017.
[13]. Luong, Minh-Thang, et al. "Multi-task sequence to sequence learning." arXiv preprint arXiv:1511.06114 (2015).
[14]. Ling, Jeffrey. Coarse-to-fine attention models for document summarization. Diss. 2017.
[15]. Rush, Alexander M., Sumit Chopra, and Jason Weston. "A neural attention model for abstractive sentence summarization." arXiv preprint arXiv:1509.00685 (2015).
[16]. Shen, Shi-Qi, et al. "Recent advances on neural headline generation." Journal of computer science and technology 32.4 (2017): 768-784.
[17]. See, Abigail, Peter J. Liu, and Christopher D. Manning. "Get to the point: Summarization with Pointer-Generator networks." arXiv preprint arXiv:1704.04368 (2017).
[18]. Singh, Anita Kumari, and Mogalla Shashi. "Vectorization of Text Documents for Identifying Unifiable News Articles." corpora 10.7 (2019).
[19]. Hermann, Karl Moritz, et al. "Teaching machines to read and comprehend." Advances in neural information processing systems. 2015.
[20]. Lin, Chin-Yew. "Rouge: A package for automatic evaluation of summaries." Text summarization branches out. 2004.
[21]. Erkan, Günes, and Dragomir R. Radev. "Lexrank: Graphbased lexical centrality as salience in text summarization." Journal of artificial intelligence research 22 (2004): 457-479. [7].
[22]. Carbonell, Jaime G., and Jade Goldstein. "The use of MMR, diversity-based reranking for reordering documents and producing summaries." SIGIR. Vol. 98. 1998.