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This paper introduces a novel document-level data augmentation framework that leverages discourse parsing and LLMs to maintain rhetorical coherence while shifting topics, addressing a key gap in existing NLP methods. Experimental results on summarization and question answering demonstrate that this approach provides effective complementary supervision for low-resource training scenarios. The work advances the state of synthetic data generation by ensuring logical flow is preserved at the paragraph level rather than just the sentence level.
Structure-Preserving Document-Level Data Augmentation via Discourse Parsing
arXiv:2610.19497v1 Announce Type: new Abstract: Data augmentation (DA) plays a vital role in improving model performance in low-resource or limited-supervision scenarios within Natural Language Processing (NLP). Existing DA methods, such as synonym replacement and back-translation, have demonstrated effectiveness on the word or sentence level; however, they frequently neglect discourse-level coherence and logical flow, which are essential for complex tasks dependent on inter-sentential relationships. In this paper, we propose a structure-preserving document-level data augmentation framework based on large language models (LLMs) and document-level discourse structure parsing. Our approach identifies rhetorical relations between sentence pairs and extracts key phrases, which are then replaced with semantically coherent but topically shifted content while preserving the original discourse structure. Experimental results on text summarization and question answering show that the proposed augmented data provides complementary supervision and improves combined-data training across the evaluated tasks. Comparisons with a standard RST parser and controlled ablations further support the proposed discourse representation.
Abstract:Data augmentation (DA) plays a vital role in improving model performance in low-resource or limited-supervision scenarios within Natural Language Processing (NLP). Existing DA methods, such as synonym replacement and back-translation, have demonstrated effectiveness on the word or sentence level; however, they frequently neglect discourse-level coherence and logical flow, which are essential for complex tasks dependent on inter-sentential relationships. In this paper, we propose a structure-preserving document-level data augmentation framework based on large language models (LLMs) and document-level discourse structure parsing. Our approach identifies rhetorical relations between sentence pairs and extracts key phrases, which are then replaced with semantically coherent but topically shifted content while preserving the original discourse structure. Experimental results on text summarization and question answering show that the proposed augmented data provides complementary supervision and improves combined-data training across the evaluated tasks. Comparisons with a standard RST parser and controlled ablations further support the proposed discourse representation.
11 pages, 1 figure, 6 tables. Accepted at the 40th Pacific Asia Conference on Language, Information and Computation (PACLIC 2026)
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2610.19497 [cs.CL]
(or arXiv:2610.19497v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.19497 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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