Modeling Unified Semantic Discourse Structure for High-quality Headline Generation
<|reference_start|>Modeling Unified Semantic Discourse Structure for High-quality Headline Generation: 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.<|reference_end|>
@article{xu2024modeling,
    title={Modeling Unified Semantic Discourse Structure for High-quality Headline
  Generation},
    author={Minghui Xu, Hao Fei, Fei Li, Shengqiong Wu, Rui Sun, Chong Teng,
  Donghong Ji},
    journal={arXiv preprint arXiv:2403.15776},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15776},
    primaryClass={cs.CL cs.AI}
}