Modeling Unified Semantic Discourse Structure for High-quality Headline Generation

• URL: http://arxiv.org/abs/2403.15776v1
• Date: Sat, 23 Mar 2024 09:18:53 GMT
• Title: Modeling Unified Semantic Discourse Structure for High-quality Headline Generation
• Authors: Minghui Xu, Hao Fei, Fei Li, Shengqiong Wu, Rui Sun, Chong Teng, Donghong Ji,
• Abstract summary: We propose using a unified semantic discourse structure (S3) to represent document semantics. The hierarchical composition of sentence, clause, and word intrinsically characterizes the semantic meaning of the overall document. Our work can be instructive for a broad range of document modeling tasks, more than headline or summarization generation.
• Score: 45.23071138765902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Headline generation aims to summarize a long document with a short, catchy title that reflects the main idea. This requires accurately capturing the core document semantics, which is challenging due to the lengthy and background information-rich na ture of the texts. In this work, We propose using a unified semantic discourse structure (S3) to represent document semantics, achieved by combining document-level rhetorical structure theory (RST) trees with sentence-level abstract meaning representation (AMR) graphs to construct S3 graphs. The hierarchical composition of sentence, clause, and word intrinsically characterizes the semantic meaning of the overall document. We then develop a headline generation framework, in which the S3 graphs are encoded as contextual features. To consolidate the efficacy of S3 graphs, we further devise a hierarchical structure pruning mechanism to dynamically screen the redundant and nonessential nodes within the graph. Experimental results on two headline generation datasets demonstrate that our method outperforms existing state-of-art methods consistently. Our work can be instructive for a broad range of document modeling tasks, more than headline or summarization generation.

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