1 code implementation • EMNLP 2021 • Lila Gravellier, Julie Hunter, Philippe Muller, Thomas Pellegrini, Isabelle Ferrané
Discourse segmentation, the first step of discourse analysis, has been shown to improve results for text summarization, translation and other NLP tasks.
no code implementations • 26 Jun 2024 • Kate Thompson, Akshay Chaturvedi, Julie Hunter, Nicholas Asher
This paper provides the first discourse parsing experiments with a large language model(LLM) finetuned on corpora annotated in the style of SDRT (Segmented Discourse Representation Theory Asher, 1993; Asher and Lascarides, 2003).
no code implementations • 17 May 2024 • Virgile Rennard, Guokan Shang, Michalis Vazirgiannis, Julie Hunter
We introduce an extractive summarization system for meetings that leverages discourse structure to better identify salient information from complex multi-party discussions.
1 code implementation • 8 Dec 2023 • Virgile Rennard, Guokan Shang, Damien Grari, Julie Hunter, Michalis Vazirgiannis
In this paper, we present a dataset of French political debates for the purpose of enhancing resources for multi-lingual dialogue summarization.
no code implementations • 28 Nov 2023 • Julie Hunter, Jérôme Louradour, Virgile Rennard, Ismaïl Harrando, Guokan Shang, Jean-Pierre Lorré
We present the Claire French Dialogue Dataset (CFDD), a resource created by members of LINAGORA Labs in the context of the OpenLLM France initiative.
no code implementations • 21 Jun 2023 • Nicholas Asher, Swarnadeep Bhar, Akshay Chaturvedi, Julie Hunter, Soumya Paul
With the advent of large language models (LLMs), the trend in NLP has been to train LLMs on vast amounts of data to solve diverse language understanding and generation tasks.
2 code implementations • 8 Aug 2022 • Virgile Rennard, Guokan Shang, Julie Hunter, Michalis Vazirgiannis
A system that could reliably identify and sum up the most important points of a conversation would be valuable in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls.
Abstractive Dialogue Summarization
Abstractive Text Summarization
+4
no code implementations • 19 Oct 2021 • Nicholas Asher, Julie Hunter
We model here an epistemic bias we call \textit{interpretive blindness} (IB).
no code implementations • LREC 2016 • Nicholas Asher, Julie Hunter, Mathieu Morey, Benamara Farah, Stergos Afantenos
This paper describes the STAC resource, a corpus of multi-party chats annotated for discourse structure in the style of SDRT (Asher and Lascarides, 2003; Lascarides and Asher, 2009).