Disentangling Instructive Information from Ranked Multiple Candidates for Multi-Document Scientific Summarization

• URL: http://arxiv.org/abs/2404.10416v1
• Date: Tue, 16 Apr 2024 09:33:07 GMT
• Title: Disentangling Instructive Information from Ranked Multiple Candidates for Multi-Document Scientific Summarization
• Authors: Pancheng Wang, Shasha Li, Dong Li, Kehan Long, Jintao Tang, Ting Wang,
• Abstract summary: This paper introduces summary candidates into Multi-Document Scientific Summarization. It uses the global information of the document set and additional guidance from the summary candidates to guide the decoding process. We observe noticeable performance improvements according to automatic and human evaluation.
• Score: 13.953968282622121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatically condensing multiple topic-related scientific papers into a succinct and concise summary is referred to as Multi-Document Scientific Summarization (MDSS). Currently, while commonly used abstractive MDSS methods can generate flexible and coherent summaries, the difficulty in handling global information and the lack of guidance during decoding still make it challenging to generate better summaries. To alleviate these two shortcomings, this paper introduces summary candidates into MDSS, utilizing the global information of the document set and additional guidance from the summary candidates to guide the decoding process. Our insights are twofold: Firstly, summary candidates can provide instructive information from both positive and negative perspectives, and secondly, selecting higher-quality candidates from multiple options contributes to producing better summaries. Drawing on the insights, we propose a summary candidates fusion framework -- Disentangling Instructive information from Ranked candidates (DIR) for MDSS. Specifically, DIR first uses a specialized pairwise comparison method towards multiple candidates to pick out those of higher quality. Then DIR disentangles the instructive information of summary candidates into positive and negative latent variables with Conditional Variational Autoencoder. These variables are further incorporated into the decoder to guide generation. We evaluate our approach with three different types of Transformer-based models and three different types of candidates, and consistently observe noticeable performance improvements according to automatic and human evaluation. More analyses further demonstrate the effectiveness of our model in handling global information and enhancing decoding controllability.

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