Laurent Charlin


2020

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Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles
Yao Lu | Yue Dong | Laurent Charlin
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and empirical results—using several state-of-the-art models trained on the Multi-XScience dataset—reveal that Multi-XScience is well suited for abstractive models.

2016

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On the Evaluation of Dialogue Systems with Next Utterance Classification
Ryan Lowe | Iulian Vlad Serban | Michael Noseworthy | Laurent Charlin | Joelle Pineau
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
Chia-Wei Liu | Ryan Lowe | Iulian Serban | Mike Noseworthy | Laurent Charlin | Joelle Pineau
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing