mlsanityData to 14 Oct 2026

DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarization

arXiv 2410.15687 ↗First posted 21 Oct 2024cs.CLcited by 4

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Abstract (arXiv)

Most research on abstractive summarization focuses on single-domain applications, often neglecting how domain shifts between documents affect performance and the generalization ability of summarization models. To address this issue, we introduce DomainSum, a hierarchical benchmark designed to capture fine-grained domain shifts in abstractive summarization. We categorize these shifts into three levels: genre, style, and topic, and demonstrate through comprehensive benchmark analysis that they follow a hierarchical structure. Furthermore, we evaluate the domain generalization capabilities of commonly used pre-trained language models (PLMs) and large language models (LLMs) in in-domain and cross-domain settings.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 39 of its 56 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

39 of 56 references.

References · 39

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27 more
  1. 2015Teaching Machines to Read and Comprehendcited by 1,049
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  3. 2019SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationcited by 535
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  17. 2021AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarizationcited by 51
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025StrucSum: Graph-Structured Reasoning for Long Document Extractive Summarization with LLMscited by 7
  2. 2026OmniTrend: Content-Context Modeling for Scalable Social Popularity Predictioncited by 1
  3. 2026Forging LLM Authorship Fingerprints with Targeted Rewritingcited by 0
  4. 2026Sentence Specificity Scores for Collaborative Technical Documentation: A Domain-Transfer Studycited by 0