Centrality Meets Centroid: A Graph-based Approach for Unsupervised Document Summarization
This work addresses the problem of generating summaries without labeled data for researchers and practitioners in NLP, though it appears incremental as it builds on existing graph-based techniques.
The paper tackles unsupervised extractive document summarization by proposing a graph-based method that selects summary candidates using centrality and matches them to a centroid, achieving state-of-the-art results on two benchmark datasets.
Unsupervised document summarization has re-acquired lots of attention in recent years thanks to its simplicity and data independence. In this paper, we propose a graph-based unsupervised approach for extractive document summarization. Instead of ranking sentences by salience and extracting sentences one by one, our approach works at a summary-level by utilizing graph centrality and centroid. We first extract summary candidates as subgraphs based on centrality from the sentence graph and then select from the summary candidates by matching to the centroid. We perform extensive experiments on two bench-marked summarization datasets, and the results demonstrate the effectiveness of our model compared to state-of-the-art baselines.