Unsupervised Summarization by Jointly Extracting Sentences and Keywords

• URL: http://arxiv.org/abs/2009.07481v2
• Date: Mon, 24 Jul 2023 03:26:17 GMT
• Title: Unsupervised Summarization by Jointly Extracting Sentences and Keywords
• Authors: Zongyi Li, Xiaoqing Zheng, Jun He
• Abstract summary: RepRank is an unsupervised graph-based ranking model for extractive multi-document summarization. We show that salient sentences and keywords can be extracted in a joint and mutual reinforcement process using our learned representations. Experiment results with multiple benchmark datasets show that RepRank achieved the best or comparable performance in ROUGE.
• Score: 12.387378783627762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present RepRank, an unsupervised graph-based ranking model for extractive multi-document summarization in which the similarity between words, sentences, and word-to-sentence can be estimated by the distances between their vector representations in a unified vector space. In order to obtain desirable representations, we propose a self-attention based learning method that represent a sentence by the weighted sum of its word embeddings, and the weights are concentrated to those words hopefully better reflecting the content of a document. We show that salient sentences and keywords can be extracted in a joint and mutual reinforcement process using our learned representations, and prove that this process always converges to a unique solution leading to improvement in performance. A variant of absorbing random walk and the corresponding sampling-based algorithm are also described to avoid redundancy and increase diversity in the summaries. Experiment results with multiple benchmark datasets show that RepRank achieved the best or comparable performance in ROUGE.

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