Unsupervised Extractive Text Summarization with Distance-Augmented Sentence Graphs
Jingzhou Liu, Dominic J. D. Hughes, Yiming Yang
Abstract
Supervised summarization has made significant improvements in recent years by leveraging cutting-edge deep learning technologies. However, the true success of supervised methods relies on the availability of large quantity of human-generated summaries of documents, which is highly costly and difficult to obtain in general. This paper proposes an unsupervised approach to extractive text summarization, which uses an automatically constructed sentence graph from each document to select salient sentences for summarization based on both the similarities and relative distances in the neighborhood of each sentences. We further generalize our approach from single-document summarization to a multi-document setting, by aggregating document-level graphs via proximity-based cross-document edges. In our experiments on benchmark datasets, the proposed approach achieved competitive or better results than previous state-of-the-art unsupervised extractive summarization methods in both single-document and multi-document settings, and the performance is competitive to strong supervised baselines.