Papers by Maxime Meyer
Tau-Eval: A Unified Evaluation Framework for Useful and Private Text Anonymization (2025.emnlp-demos)
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| Challenge: | Existing studies on text anonymization prioritize privacy preservation at the expense of utility, relying on reference-based metrics like ROUGE, BERTScore, or METEOR to measure textual fidelity. |
| Approach: | They propose an open-source framework for benchmarking text anonymization methods through the lens of privacy and utility task sensitivity. |
| Outcome: | The proposed framework is open-source and provides a Python library, documentation and tutorials. |
Live Blog Corpus for Summarization (L18-1)
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| Challenge: | Live blogs are increasingly popular news format to cover breaking news and live events. |
| Approach: | They propose to collect corpora for automatic live blog summarization by a web-based system . they make the tools publicly available to encourage the research community . |
| Outcome: | The proposed method improves the accuracy of live blog summarization by allowing for public access to the corpus. |
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance (D19-1)
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| Challenge: | Existing evaluation metrics are not capable of evaluating text quality. |
| Approach: | They propose a metric that compares system output against reference texts based on semantics rather than surface forms. |
| Outcome: | The proposed metric shows a high correlation with human judgment of text quality on a number of text generation tasks. |
Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization (2026.findings-acl)
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| Challenge: | Existing methods for anonymizing textual documents lack flexibility to adapt to diverse requirements. |
| Approach: | They propose a task formulation in which anonymization strategies are automatically adapted to specific privacy–utility requirements. |
| Outcome: | The proposed framework achieves better privacy–utility trade-off than existing baselines on open-source language models while remaining computationally efficient and effective on larger closed-source models. |