Unsupervised Extractive Summarization with Learnable Length Control Strategies

• URL: http://arxiv.org/abs/2312.06901v2
• Date: Mon, 18 Dec 2023 09:05:24 GMT
• Title: Unsupervised Extractive Summarization with Learnable Length Control Strategies
• Authors: Renlong Jie, Xiaojun Meng, Xin Jiang, Qun Liu
• Abstract summary: Unsupervised extractive summarization is an important technique in information extraction and retrieval. Most of existing unsupervised methods rely on graph-based ranking on sentence centrality. This paper introduces an unsupervised extractive summarization model based on a siamese network.
• Score: 33.75745103050596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised extractive summarization is an important technique in information extraction and retrieval. Compared with supervised method, it does not require high-quality human-labelled summaries for training and thus can be easily applied for documents with different types, domains or languages. Most of existing unsupervised methods including TextRank and PACSUM rely on graph-based ranking on sentence centrality. However, this scorer can not be directly applied in end-to-end training, and the positional-related prior assumption is often needed for achieving good summaries. In addition, less attention is paid to length-controllable extractor, where users can decide to summarize texts under particular length constraint. This paper introduces an unsupervised extractive summarization model based on a siamese network, for which we develop a trainable bidirectional prediction objective between the selected summary and the original document. Different from the centrality-based ranking methods, our extractive scorer can be trained in an end-to-end manner, with no other requirement of positional assumption. In addition, we introduce a differentiable length control module by approximating 0-1 knapsack solver for end-to-end length-controllable extracting. Experiments show that our unsupervised method largely outperforms the centrality-based baseline using a same sentence encoder. In terms of length control ability, via our trainable knapsack module, the performance consistently outperforms the strong baseline without utilizing end-to-end training. Human evaluation further evidences that our method performs the best among baselines in terms of relevance and consistency.

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