Noémie Elhadad

Also published as: Noemie Elhadad


2022

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Learning to Revise References for Faithful Summarization
Griffin Adams | Han-Chin Shing | Qing Sun | Christopher Winestock | Kathleen McKeown | Noémie Elhadad
Findings of the Association for Computational Linguistics: EMNLP 2022

In real-world scenarios with naturally occurring datasets, reference summaries are noisy and may contain information that cannot be inferred from the source text. On large news corpora, removing low quality samples has been shown to reduce model hallucinations. Yet, for smaller, and/or noisier corpora, filtering is detrimental to performance. To improve reference quality while retaining all data, we propose a new approach: to selectively re-write unsupported reference sentences to better reflect source data. We automatically generate a synthetic dataset of positive and negative revisions by corrupting supported sentences and learn to revise reference sentences with contrastive learning. The intensity of revisions is treated as a controllable attribute so that, at inference, diverse candidates can be over-generated-then-rescored to balance faithfulness and abstraction. To test our methods, we extract noisy references from publicly available MIMIC-III discharge summaries for the task of hospital-course summarization, and vary the data on which models are trained. According to metrics and human evaluation, models trained on revised clinical references are much more faithful, informative, and fluent than models trained on original or filtered data.

2021

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What’s in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization
Griffin Adams | Emily Alsentzer | Mert Ketenci | Jason Zucker | Noémie Elhadad
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Summarization of clinical narratives is a long-standing research problem. Here, we introduce the task of hospital-course summarization. Given the documentation authored throughout a patient’s hospitalization, generate a paragraph that tells the story of the patient admission. We construct an English, text-to-text dataset of 109,000 hospitalizations (2M source notes) and their corresponding summary proxy: the clinician-authored “Brief Hospital Course” paragraph written as part of a discharge note. Exploratory analyses reveal that the BHC paragraphs are highly abstractive with some long extracted fragments; are concise yet comprehensive; differ in style and content organization from the source notes; exhibit minimal lexical cohesion; and represent silver-standard references. Our analysis identifies multiple implications for modeling this complex, multi-document summarization task.

2015

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SemEval-2015 Task 14: Analysis of Clinical Text
Noémie Elhadad | Sameer Pradhan | Sharon Gorman | Suresh Manandhar | Wendy Chapman | Guergana Savova
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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A convex and feature-rich discriminative approach to dependency grammar induction
Édouard Grave | Noémie Elhadad
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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SemEval-2014 Task 7: Analysis of Clinical Text
Sameer Pradhan | Noémie Elhadad | Wendy Chapman | Suresh Manandhar | Guergana Savova
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Cross-narrative Temporal Ordering of Medical Events
Preethi Raghavan | Eric Fosler-Lussier | Noémie Elhadad | Albert M. Lai
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Terminology Questions in Texts Authored by Patients
Noemie Elhadad
Proceedings of the 4th International Workshop on Computational Terminology (Computerm)

2011

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Putting it Simply: a Context-Aware Approach to Lexical Simplification
Or Biran | Samuel Brody | Noémie Elhadad
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Cancer Stage Prediction Based on Patient Online Discourse
Mukund Jha | Noémie Elhadad
Proceedings of the 2010 Workshop on Biomedical Natural Language Processing

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A Comparison of Features for Automatic Readability Assessment
Lijun Feng | Martin Jansche | Matt Huenerfauth | Noémie Elhadad
Coling 2010: Posters

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An Unsupervised Aspect-Sentiment Model for Online Reviews
Samuel Brody | Noemie Elhadad
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2009

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Cognitively Motivated Features for Readability Assessment
Lijun Feng | Noémie Elhadad | Matt Huenerfauth
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

2007

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Mining a Lexicon of Technical Terms and Lay Equivalents
Noemie Elhadad | Komal Sutaria
Biological, translational, and clinical language processing

2003

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Sentence Alignment for Monolingual Comparable Corpora
Regina Barzilay | Noemie Elhadad
Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing

2002

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Collection and linguistic processing of a large-scale corpus of medical articles
Simone Teufel | Noemie Elhadad
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2001

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Sentence Ordering in Multidocument Summarization
Regina Barzilay | Noemie Elhadad | Kathleen R. McKeown
Proceedings of the First International Conference on Human Language Technology Research