🧑‍🔬 Henghui Zhu

🏛️ Affiliations

🎓Boston UniversityBoston · United States2016–2020 · 23 papers🏢AmazonSeattle · United States2020–2025 · 22 papers🎓Columbia UniversityNew York · United States2020–2021 · 7 papers🔬Academy of Mathematics and Systems ScienceBeijing · China2014–2015 · 7 papers🏛️Chinese Academy of SciencesBeijing · China2014–2015 · 6 papers🎓University of Massachusetts AmherstAmherst Center · United States2024–2025 · 2 papers🎓Chinese University of Hong KongHong Kong · Hong Kong2020–2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

🧭 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Supporting Clustering with Contrastive Learning (2021 · 239 citations)
   Deep clustering methods that rely on distances often fail when semantic categories overlap heavily in the embedding space at the start of training.
2. Entity-level Factual Consistency of Abstractive Text Summarization (2021 · 205 citations)
   Abstractive summarizers often hallucinate named entities, inserting people, places or organizations that never occur in the source article. ROUGE does not detect this kind of error. The authors address it with three entity-level metrics computed with spaCy NER and lenient, case-insensitive n-gram matching:
3. Improving Factual Consistency of Abstractive Summarization via Question Answering (2021 · 128 citations)
   Abstractive summarizers such as fine-tuned BART-large often produce fluent summaries that are not supported by the source document or even contradict it. Standard MLE training rewards n-gram overlap, not faithfulness. The authors tackle this problem in two steps.

All works

5 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. Supporting Clustering with Contrastive LearningConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Dejiao Zhang, Feng Nan, Xiaokai Wei, et al.2021 · 239 citations
2. Entity-level Factual Consistency of Abstractive Text SummarizationConference of the European Chapter of the Association for Computational Linguistics (EACL)Feng Nan, Ramesh Nallapati, Zhiguo Wang, et al.2021 · 205 citations
3. Improving Factual Consistency of Abstractive Summarization via Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL)Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, et al.2021 · 128 citations
4. PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable QueriesProceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · original ↗ · no summary yetMingwen Dong et al.2025 · 15 citations
5. DSMentor: Curriculum-Guided Inference with Online Memory for Data-Science LLM AgentsProceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era (SURGeLLM 2026) · original ↗ · no summary yetHe Wang et al.2026

🤝 Co-authors here

10 co-authors with a page, the most papers together first.

• Bing Xiang ×3
• Dejiao Zhang ×3
• Feng Nan ×3
• Kathleen McKeown ×3
• Ramesh Nallapati ×3
• Andrew O. Arnold ×2
• Cicero Nogueira dos Santos ×2
• Zhiguo Wang ×2
• Patrick Ng
• Shang-Wen Li

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2021
• Conference of the European Chapter of the Association for Computational Linguistics (EACL): EACL 2021
• Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): NAACL 2021
• Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
• Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era (SURGeLLM 2026)

🔗 Cited by

Papers of our library cite their works 207 times (of the 141,840 papers whose bibliographies we read); 17 citing papers with a page here →.

Updated 24 October 2026

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