Papers by Virgile Rennard
Abstractive Meeting Summarization: A Survey (2023.tacl-1)
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| Challenge: | Recent advances in deep learning have improved language generation systems, opening the door to improved forms of abstractive summarization. |
| Approach: | They propose to use neural encoder-decoder architectures to generate abstractive meeting summarizations that are particularly well-suited for multi-party conversation. |
| Outcome: | The proposed system could be used in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls. |
FREDSum: A Dialogue Summarization Corpus for French Political Debates (2023.findings-emnlp)
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| Challenge: | Recent advances in deep learning have improved the performance of abstractive summarization systems. |
| Approach: | They present a dataset of french political debates to enhance resources for multi-lingual dialogue summarization. |
| Outcome: | The proposed dataset will be made publicly available for use by the research community. |
Bias in the Mirror : Are LLMs opinions robust to their own adversarial attacks (2025.acl-long)
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| Challenge: | Existing work on large language models lacks robustness, highlighting the limitations of such models. |
| Approach: | They propose a novel approach where two LLMs engage in self-debate to persuade a neutral version of the model. |
| Outcome: | The proposed approach examines whether large language models are robust during interactions and whether they are susceptible to reinforcing misinformation or shifting to harmful viewpoints. |
Automatic Analysis of Substantiation in Scientific Peer Reviews (2023.findings-emnlp)
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| Challenge: | Existing systems to analyze peer reviews' quality are inadequate due to the increasing workload of reviewers and the lack of domain experts . |
| Approach: | They propose to use a claim-evidence pair extraction problem to analyze substantiation in peer reviews and train an argument mining system to do the same. |
| Outcome: | The proposed system could be used by conference managers and reviewers to analyze the quality of peer reviews. |