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        <title>Darbarer @ AutoMin2023: Transcription simplification for concise minute generation from multi-party conversations</title>
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        <namePart type="given">Ismaël</namePart>
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    <abstract>This document reports the approach of our team Darbarer for the main task (Task A) of the AutoMin 2023 challenge. Our system is composed of four main modules. The first module relies on a text simplification model aiming at standardizing the utterances of the conversation and compressing the input in order to focus on informative content. The second module handles summarization by employing a straightforward segmentation strategy and a fine-tuned BART-based generative model. Then a titling module has been trained in order to propose a short description of each summarized block. Lastly, we apply a post-processing step aimed at enhancing readability through specific formatting rules. Our contributions lie in the first, third and last steps. Our system generates precise and concise minutes. We provide a detailed description of our modules, discuss the difficulty of evaluating their impact and propose an analysis of observed errors in our generated minutes.</abstract>
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%0 Conference Proceedings
%T Darbarer @ AutoMin2023: Transcription simplification for concise minute generation from multi-party conversations
%A Rousseau, Ismaël
%A Fosse, Loïc
%A Dkhissi, Youness
%A Damnati, Geraldine
%A Lecorvé, Gwénolé
%Y Mille, Simon
%S Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F rousseau-etal-2023-darbarer
%X This document reports the approach of our team Darbarer for the main task (Task A) of the AutoMin 2023 challenge. Our system is composed of four main modules. The first module relies on a text simplification model aiming at standardizing the utterances of the conversation and compressing the input in order to focus on informative content. The second module handles summarization by employing a straightforward segmentation strategy and a fine-tuned BART-based generative model. Then a titling module has been trained in order to propose a short description of each summarized block. Lastly, we apply a post-processing step aimed at enhancing readability through specific formatting rules. Our contributions lie in the first, third and last steps. Our system generates precise and concise minutes. We provide a detailed description of our modules, discuss the difficulty of evaluating their impact and propose an analysis of observed errors in our generated minutes.
%U https://aclanthology.org/2023.inlg-genchal.17
%P 121-131

Markdown (Informal)

[Darbarer @ AutoMin2023: Transcription simplification for concise minute generation from multi-party conversations](https://aclanthology.org/2023.inlg-genchal.17) (Rousseau et al., INLG-SIGDIAL 2023)

• Darbarer @ AutoMin2023: Transcription simplification for concise minute generation from multi-party conversations (Rousseau et al., INLG-SIGDIAL 2023)

ACL

• Ismaël Rousseau, Loïc Fosse, Youness Dkhissi, Geraldine Damnati, and Gwénolé Lecorvé. 2023. Darbarer @ AutoMin2023: Transcription simplification for concise minute generation from multi-party conversations. In Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges, pages 121–131, Prague, Czechia. Association for Computational Linguistics.