MOO-CMDS+NER: Named Entity Recognition-Based Extractive Comment-Oriented Multi-document Summarization - Archive ouverte HAL
Communication Dans Un Congrès Année : 2023

MOO-CMDS+NER: Named Entity Recognition-Based Extractive Comment-Oriented Multi-document Summarization

Vishal Singh Roha
Naveen Saini
Sriparna Saha

Résumé

In this work, we propose an unsupervised extractive summarization framework for generating good quality summaries which are supplemented by the comments posted by the end-users. Using the evolutionary multi-objective optimization concept, different objective functions for assessing the quality of a summary, like diversity and the relevance of sentences in relation to comments, are optimized simultaneously. In the literature, named entity recognition (NER) has been shown to be useful in the summarization process. The current work is the first of its kind where we have introduced a new objective function that utilizes the concept of NER in news documents and user comments to score the news sentences. To test how well the new objective function works, different combinations of the NER-based objective function with already existing objective functions were tested on the English and French datasets using ROUGE 1, 2, and SU4 F1-scores. We have also investigated the abstractive and compressive summarization approaches for our comparative analysis. The code of the proposed work is available at the github repository https://github.com/vishalsinghroha/Unsupervised-Comment-based-Multi-document-Extractive-Summarization .
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Dates et versions

hal-04750053 , version 1 (23-10-2024)

Identifiants

Citer

Vishal Singh Roha, Naveen Saini, Sriparna Saha, Jose G. Moreno. MOO-CMDS+NER: Named Entity Recognition-Based Extractive Comment-Oriented Multi-document Summarization. 45th European Conference on Information Retrieval, ECIR 2023, Apr 2023, Dublin, Ireland. pp.580-588, ⟨10.1007/978-3-031-28238-6_49⟩. ⟨hal-04750053⟩
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