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        <title>CAWESumm: A Contextual and Anonymous Walk Embedding Based Extractive Summarization of Legal Bills</title>
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        <namePart type="given">Deepali</namePart>
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    <abstract>Extractive summarization of lengthy legal documents requires an appropriate sentence scoring mechanism. This mechanism should capture both the local semantics of a sentence as well as the global document-level context of a sentence. The search for an appropriate sentence embedding that can enable an effective scoring mechanism has been the focus of several research works in this domain. In this work, we propose an improved sentence embedding approach that combines a Legal Bert-based local embedding of the sentence with an anonymous random walk-based entire document embedding. Such combined features help effectively capture the local and global information present in a sentence. The experimental results suggest that the proposed sentence embedding approach can be very beneficial for the appropriate representation of sentences in legal documents, improving the sentence scoring mechanism required for extractive summarization of these documents.</abstract>
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%0 Conference Proceedings
%T CAWESumm: A Contextual and Anonymous Walk Embedding Based Extractive Summarization of Legal Bills
%A Jain, Deepali
%A Borah, Malaya Dutta
%A Biswas, Anupam
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F jain-etal-2021-cawesumm
%X Extractive summarization of lengthy legal documents requires an appropriate sentence scoring mechanism. This mechanism should capture both the local semantics of a sentence as well as the global document-level context of a sentence. The search for an appropriate sentence embedding that can enable an effective scoring mechanism has been the focus of several research works in this domain. In this work, we propose an improved sentence embedding approach that combines a Legal Bert-based local embedding of the sentence with an anonymous random walk-based entire document embedding. Such combined features help effectively capture the local and global information present in a sentence. The experimental results suggest that the proposed sentence embedding approach can be very beneficial for the appropriate representation of sentences in legal documents, improving the sentence scoring mechanism required for extractive summarization of these documents.
%U https://aclanthology.org/2021.icon-main.50
%P 414-422

Markdown (Informal)

[CAWESumm: A Contextual and Anonymous Walk Embedding Based Extractive Summarization of Legal Bills](https://aclanthology.org/2021.icon-main.50) (Jain et al., ICON 2021)

• CAWESumm: A Contextual and Anonymous Walk Embedding Based Extractive Summarization of Legal Bills (Jain et al., ICON 2021)

ACL

• Deepali Jain, Malaya Dutta Borah, and Anupam Biswas. 2021. CAWESumm: A Contextual and Anonymous Walk Embedding Based Extractive Summarization of Legal Bills. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 414–422, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).