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Recent advances in natural language processing have enabled automation of awide range of tasks, including machine translation, named entity recognition,and sentiment analysis. Automated summarization of documents, or groups ofdocuments, however, has remained elusive, with many efforts limited toextraction of keywords, key phrases, or key sentences. Accurate abstractivesummarization has yet to be achieved due to the inherent difficulty of theproblem, and limited availability of training data. In this paper, we propose atopic-centric unsupervised multi-document summarization framework to generateextractive and abstractive summaries for groups of scientific articles across20 Fields of Study (FoS) in Microsoft Academic Graph (MAG) and news articlesfrom DUC-2004 Task 2. The proposed algorithm generates an abstractive summaryby developing salient language unit selection and text generation techniques.Our approach matches the state-of-the-art when evaluated on automatedextractive evaluation metrics and performs better for abstractive summarizationon five human evaluation metrics (entailment, coherence, conciseness,readability, and grammar). We achieve a kappa score of 0.68 between twoco-author linguists who evaluated our results. We plan to publicly shareMAG-20, a human-validated gold standard dataset of topic-clustered researcharticles and their summaries to promote research in abstractive summarization.

Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles