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