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        <title>Non-Parametric Memory Guidance for Multi-Document Summarization</title>
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        <namePart type="given">Florian</namePart>
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            <title>Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Multi-document summarization (MDS) is a difficult task in Natural Language Processing, aiming to summarize information from several documents. However, the source documents are often insufficient to obtain a qualitative summary. We propose a retriever-guided model combined with non-parametric memory for summary generation. This model retrieves relevant candidates from a database and then generates the summary considering the candidates with a copy mechanism and the source documents. The retriever is implemented with Approximate Nearest Neighbor Search (ANN) to search large databases. Our method is evaluated on the MultiXScience dataset which includes scientific articles. Finally, we discuss our results and possible directions for future work.</abstract>
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        <url>https://aclanthology.org/2023.ranlp-1.17</url>
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%0 Conference Proceedings
%T Non-Parametric Memory Guidance for Multi-Document Summarization
%A Baud, Florian
%A Aussem, Alex
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F baud-aussem-2023-non
%X Multi-document summarization (MDS) is a difficult task in Natural Language Processing, aiming to summarize information from several documents. However, the source documents are often insufficient to obtain a qualitative summary. We propose a retriever-guided model combined with non-parametric memory for summary generation. This model retrieves relevant candidates from a database and then generates the summary considering the candidates with a copy mechanism and the source documents. The retriever is implemented with Approximate Nearest Neighbor Search (ANN) to search large databases. Our method is evaluated on the MultiXScience dataset which includes scientific articles. Finally, we discuss our results and possible directions for future work.
%U https://aclanthology.org/2023.ranlp-1.17
%P 153-158

Markdown (Informal)

[Non-Parametric Memory Guidance for Multi-Document Summarization](https://aclanthology.org/2023.ranlp-1.17) (Baud & Aussem, RANLP 2023)

• Non-Parametric Memory Guidance for Multi-Document Summarization (Baud & Aussem, RANLP 2023)

ACL

• Florian Baud and Alex Aussem. 2023. Non-Parametric Memory Guidance for Multi-Document Summarization. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 153–158, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.