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

Understand

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.