Fine-Tuning Textrank for Legal Document Summarization: A Bayesian Optimization Based Approach-Reference-Cited by-同舟云学术

Fine-Tuning Textrank for Legal Document Summarization: A Bayesian Optimization Based Approach

Published:2020-12-16 Issue: Volume: Page:
ISSN:
Container-title:Forum for Information Retrieval Evaluation
language:
Short-container-title:

Author:

Jain Deepali¹,Borah Malaya Dutta¹,Biswas Anupam¹

Affiliation:

1. National Institute of Technology Silchar, India

Funder

Science and Engineering Research Board (SERB), Department of Science and Technology (DST) of the Government of India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3441501.3441502

Reference41 articles.

1. Federico Barrios Federico López Luis Argerich and Rosa Wachenchauzer. 2016. Variations of the similarity function of textrank for automated summarization. arXiv preprint arXiv:1602.03606(2016). Federico Barrios Federico López Luis Argerich and Rosa Wachenchauzer. 2016. Variations of the similarity function of textrank for automated summarization. arXiv preprint arXiv:1602.03606(2016).

2. A Comparative Study of Summarization Algorithms Applied to Legal Case Judgments

Cited by 20 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Judgment prediction from legal documents using Texas wolf optimization based deep BiLSTM model;Intelligent Decision Technologies;2024-06-07

2. Summarization of Lengthy Legal Documents via Abstractive Dataset Building: An Extract-then-Assign Approach;Expert Systems with Applications;2024-03

3. Improving YOLOv6 using advanced PSO optimizer for weight selection in lung cancer detection and classification;Multimedia Tools and Applications;2024-02-24

4. A Comprehensive Review on Automatic Text Summarization;Computación y Sistemas;2023-12-27

5. Extractive social media text summarization based on MFMMR-BertSum;Array;2023-12