Ensemble methods for improving extractive summarization of legal case judgements
Summarization of legal case judgement documents is a practical and challenging problem, for which many summarization algorithms of different varieties have been tried. In this work, rather than developing yet another summarization algorithm, we investigate if intelligently ensembling (combining) the...
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| Published in | Artificial intelligence and law Vol. 32; no. 1; pp. 231 - 289 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.03.2024
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0924-8463 1572-8382 |
| DOI | 10.1007/s10506-023-09349-8 |
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| Abstract | Summarization of legal case judgement documents is a practical and challenging problem, for which many summarization algorithms of different varieties have been tried. In this work, rather than developing yet another summarization algorithm, we investigate if intelligently ensembling (combining) the outputs of multiple (base) summarization algorithms can lead to better summaries of legal case judgements than any of the base algorithms. Using two datasets of case judgement documents from the Indian Supreme Court, one with extractive gold standard summaries and the other with abstractive gold standard summaries, we apply various ensembling techniques on summaries generated by a wide variety of summarization algorithms. The ensembling methods applied range from simple voting-based methods to ranking-based and graph-based ensembling methods. We show that many of our ensembling methods yield summaries that are better than the summaries produced by any of the individual base algorithms, in terms of ROUGE and METEOR scores. |
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| AbstractList | Summarization of legal case judgement documents is a practical and challenging problem, for which many summarization algorithms of different varieties have been tried. In this work, rather than developing yet another summarization algorithm, we investigate if intelligently ensembling (combining) the outputs of multiple (base) summarization algorithms can lead to better summaries of legal case judgements than any of the base algorithms. Using two datasets of case judgement documents from the Indian Supreme Court, one with extractive gold standard summaries and the other with abstractive gold standard summaries, we apply various ensembling techniques on summaries generated by a wide variety of summarization algorithms. The ensembling methods applied range from simple voting-based methods to ranking-based and graph-based ensembling methods. We show that many of our ensembling methods yield summaries that are better than the summaries produced by any of the individual base algorithms, in terms of ROUGE and METEOR scores. |
| Audience | Professional |
| Author | Deroy, Aniket Ghosh, Kripabandhu Ghosh, Saptarshi |
| Author_xml | – sequence: 1 givenname: Aniket orcidid: 0000-0001-7190-5040 surname: Deroy fullname: Deroy, Aniket email: roydanik18@kgpian.iitkgp.ac.in organization: Computer Science and Engineering, IIT Kharagpur – sequence: 2 givenname: Kripabandhu surname: Ghosh fullname: Ghosh, Kripabandhu organization: Computational and Data Sciences, IISER Kolkata – sequence: 3 givenname: Saptarshi surname: Ghosh fullname: Ghosh, Saptarshi organization: Computer Science and Engineering, IIT Kharagpur |
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| CitedBy_id | crossref_primary_10_1007_s10506_024_09394_x crossref_primary_10_1007_s44443_025_00022_5 crossref_primary_10_1007_s10506_024_09411_z crossref_primary_10_1109_ACCESS_2025_3545419 |
| Cites_doi | 10.1007/978-3-030-15712-8_27 10.18653/v1/K17-1021 10.1145/3462757.3466098 10.1145/2623330.2623732 10.1007/s11704-019-8208-z 10.1145/3322640.3326728 10.1145/3462757.3466092 10.1016/j.ipm.2004.04.003 10.1016/j.ipm.2017.11.002 10.1109/IDEA49133.2020.9170675 10.1609/aaai.v31i1.10958 10.1109/MIS.2018.033001411 10.1613/jair.1523 10.3233/FAIA210322 10.1523/JNEUROSCI.0002-08.2008 10.1145/2939672.2939754 10.1561/9781601984715 10.1145/3322640.3326715 10.18653/v1/D18-1449 10.1162/tacl_a_00373 10.1109/ICMLC.2015.7340924 10.1145/345966.345982 10.1109/ICCES.2012.6408498 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. COPYRIGHT 2024 Springer |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: COPYRIGHT 2024 Springer |
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| Keywords | Legal case judgement summarization Ensemble summarization Extractive summarization Unsupervised and supervised summarization |
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| References | Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 701–710 Zhong L, Zhong Z, Zhao Z, et al (2019) Automatic summarization of legal decisions using iterative masking of predictive sentences. In: Proceedings of ICAIL Li K, Han Y (2010) Study of selective ensemble learning method and its diversity based on decision tree and neural network. In: Proceedings of Chinese control and decision conference, pp 1310–1315 Deroy A, Bhattacharya P, Ghosh K, et al (2021) An analytical study of algorithmic and expert summaries of legal cases. In: Legal knowledge and information systems. IOS Press, pp 90–99 DuttaSChandraVMehraKEnsemble algorithms for microblog summarizationIEEE Intell Syst201833341410.1109/MIS.2018.033001411 FabbriARKryścińskiWMcCannBSummEval: re-evaluating summarization evaluationTrans Assoc Comput Linguist2021939140910.1162/tacl_a_00373 KleinbergJMHubs, authorities, and communitiesACM Comput Surv (CSUR)1999315710.1145/345966.345982 Bhattacharya P, Hiware K, Rajgaria S, et al (2019) A comparative study of summarization algorithms applied to legal case judgments. In: ECIR MehtaPMajumderPEffective aggregation of various summarization techniquesInf Process Manage201854214515810.1016/j.ipm.2017.11.002 Xu H, Savelka J, Ashley KD (2021) Toward summarizing case decisions via extracting argument issues, reasons, and conclusions. In: Proceedings of the international conference on artificial intelligence and law (ICAIL), pp 250–254 Polsley S, Jhunjhunwala P, Huang R (2016) Casesummarizer: A system for automated summarization of legal texts. In: COLING He Z, Chen C, Bu J, et al (2012) Document summarization based on data reconstruction. In: AAAI Nallapati R, Zhai F, Zhou B (2017) Summarunner: A recurrent neural network based sequence model for extractive summarization of documents. In: Proceedings of AAAI international conference Bhattacharya P, Poddar S, Rudra K, et al (2021) Incorporating domain knowledge for extractive summarization of legal case documents. In: Proc. international conference on artificial intelligence and law Lin CY (2004) ROUGE: A package for automatic evaluation of summaries. In: Text summarization branches out, pp 74–81 MaslovSRednerSPromise and pitfalls of extending google’s pagerank algorithm to citation networksJ Neurosci2008284411,10311,1051:CAS:528:DC%2BD1cXhtlGltrbN10.1523/JNEUROSCI.0002-08.2008 ErkanGRadevDRLexrank: graph-based lexical centrality as salience in text summarizationJ Artif Intell Res20042245747910.1613/jair.1523 MallickCDasAKDingWEnsemble summarization of bio-medical articles integrating clustering and multi-objective evolutionary algorithmsAppl Soft Comput2021106107347 Moawad I, Aref M (2012) Semantic graph reduction approach for abstractive text summarization. In: International conference on computer engineering and systems, pp 132–138 Liu Y (2019) Fine-tune BERT for extractive summarization. ArXiv:1903.10318 DongXYuZCaoWA survey on ensemble learningFront Comp Sci20191424125810.1007/s11704-019-8208-z Farzindar A, Lapalme G (2004) Letsum, an automatic legal text summarizing system. In: JURIX MohammadiMRezaeiJEnsemble ranking: aggregation of rankings produced by different multi-criteria decision-making methodsOmega202096102254 Rincy TN, Gupta R (2020) Ensemble learning techniques and its efficiency in machine learning: a survey. In: International conference on data, engineering and applications (IDEA), pp 1–6 Ali S, Tirumala SS, Sarrafzadeh A (2015) Ensemble learning methods for decision making: Status and future prospects. In: Proceedings of international conference on machine learning and cybernetics (ICMLC), pp 211–216 Page L, Brin S, Motwani R et al (1999) The pagerank citation ranking: Bringing order to the web. Tech. rep, Stanford InfoLab YehJYKeHRYangWPText summarization using a trainable summarizer and latent semantic analysisInf Process Manage200541759510.1016/j.ipm.2004.04.003 Collins E, Augenstein I, Riedel S (2017) A supervised approach to extractive summarisation of scientific papers. In: Proceedings of the 21st conference on computational natural language learning (CoNLL 2017), pp 195–205 Banerjee S, Lavie A (2005) METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pp 65–72 Kobayashi H (2018) Frustratingly easy model ensemble for abstractive summarization. In: Proceedings of the conference on empirical methods in natural language processing, pp 4165–4176 Liu CL, Chen KC (2019) Extracting the gist of Chinese judgments of the supreme court. In: ICAIL Saravanan M, Ravindran B, Raman S (2006) Improving legal document summarization using graphical models. In: Proceedings of the 2006 conference on legal knowledge and information systems: JURIX 2006: the nineteenth annual conference. IOS Press, NLD, pp 51–60 Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 855–864 Nenkova A, Maskey S, Liu Y (2011) Automatic summarization. In: Proceedings of ACL Shukla A, Bhattacharya P, Poddar S, et al (2022) Legal case document summarization: extractive and abstractive methods and their evaluation. In: Proceedings of the conference of the Asia-Pacific chapter of the association for computational linguistics and the international joint conference on natural language processing (Volume 1: Long Papers), pp 1048–1064 C Mallick (9349_CR20) 2021; 106 AR Fabbri (9349_CR10) 2021; 9 9349_CR3 9349_CR15 9349_CR4 9349_CR5 9349_CR13 9349_CR35 9349_CR6 9349_CR12 9349_CR19 S Dutta (9349_CR8) 2018; 33 9349_CR18 9349_CR1 9349_CR17 9349_CR2 9349_CR16 9349_CR11 9349_CR33 9349_CR32 9349_CR31 9349_CR30 M Mohammadi (9349_CR24) 2020; 96 G Erkan (9349_CR9) 2004; 22 X Dong (9349_CR7) 2019; 14 P Mehta (9349_CR22) 2018; 54 9349_CR26 9349_CR25 9349_CR23 9349_CR29 9349_CR28 9349_CR27 JY Yeh (9349_CR34) 2005; 41 S Maslov (9349_CR21) 2008; 28 JM Kleinberg (9349_CR14) 1999; 31 |
| References_xml | – reference: Nenkova A, Maskey S, Liu Y (2011) Automatic summarization. In: Proceedings of ACL – reference: FabbriARKryścińskiWMcCannBSummEval: re-evaluating summarization evaluationTrans Assoc Comput Linguist2021939140910.1162/tacl_a_00373 – reference: YehJYKeHRYangWPText summarization using a trainable summarizer and latent semantic analysisInf Process Manage200541759510.1016/j.ipm.2004.04.003 – reference: KleinbergJMHubs, authorities, and communitiesACM Comput Surv (CSUR)1999315710.1145/345966.345982 – reference: Bhattacharya P, Poddar S, Rudra K, et al (2021) Incorporating domain knowledge for extractive summarization of legal case documents. In: Proc. international conference on artificial intelligence and law – reference: Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 855–864 – reference: Lin CY (2004) ROUGE: A package for automatic evaluation of summaries. In: Text summarization branches out, pp 74–81 – reference: Banerjee S, Lavie A (2005) METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pp 65–72 – reference: Farzindar A, Lapalme G (2004) Letsum, an automatic legal text summarizing system. In: JURIX – reference: Moawad I, Aref M (2012) Semantic graph reduction approach for abstractive text summarization. In: International conference on computer engineering and systems, pp 132–138 – reference: Li K, Han Y (2010) Study of selective ensemble learning method and its diversity based on decision tree and neural network. In: Proceedings of Chinese control and decision conference, pp 1310–1315 – reference: Polsley S, Jhunjhunwala P, Huang R (2016) Casesummarizer: A system for automated summarization of legal texts. In: COLING – reference: Shukla A, Bhattacharya P, Poddar S, et al (2022) Legal case document summarization: extractive and abstractive methods and their evaluation. In: Proceedings of the conference of the Asia-Pacific chapter of the association for computational linguistics and the international joint conference on natural language processing (Volume 1: Long Papers), pp 1048–1064 – reference: Zhong L, Zhong Z, Zhao Z, et al (2019) Automatic summarization of legal decisions using iterative masking of predictive sentences. In: Proceedings of ICAIL – reference: Deroy A, Bhattacharya P, Ghosh K, et al (2021) An analytical study of algorithmic and expert summaries of legal cases. In: Legal knowledge and information systems. IOS Press, pp 90–99 – reference: Kobayashi H (2018) Frustratingly easy model ensemble for abstractive summarization. In: Proceedings of the conference on empirical methods in natural language processing, pp 4165–4176 – reference: He Z, Chen C, Bu J, et al (2012) Document summarization based on data reconstruction. In: AAAI – reference: ErkanGRadevDRLexrank: graph-based lexical centrality as salience in text summarizationJ Artif Intell Res20042245747910.1613/jair.1523 – reference: Liu Y (2019) Fine-tune BERT for extractive summarization. ArXiv:1903.10318 – reference: Nallapati R, Zhai F, Zhou B (2017) Summarunner: A recurrent neural network based sequence model for extractive summarization of documents. In: Proceedings of AAAI international conference – reference: Ali S, Tirumala SS, Sarrafzadeh A (2015) Ensemble learning methods for decision making: Status and future prospects. In: Proceedings of international conference on machine learning and cybernetics (ICMLC), pp 211–216 – reference: Liu CL, Chen KC (2019) Extracting the gist of Chinese judgments of the supreme court. In: ICAIL – reference: Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 701–710 – reference: Xu H, Savelka J, Ashley KD (2021) Toward summarizing case decisions via extracting argument issues, reasons, and conclusions. In: Proceedings of the international conference on artificial intelligence and law (ICAIL), pp 250–254 – reference: MehtaPMajumderPEffective aggregation of various summarization techniquesInf Process Manage201854214515810.1016/j.ipm.2017.11.002 – reference: Rincy TN, Gupta R (2020) Ensemble learning techniques and its efficiency in machine learning: a survey. In: International conference on data, engineering and applications (IDEA), pp 1–6 – reference: Bhattacharya P, Hiware K, Rajgaria S, et al (2019) A comparative study of summarization algorithms applied to legal case judgments. In: ECIR – reference: DongXYuZCaoWA survey on ensemble learningFront Comp Sci20191424125810.1007/s11704-019-8208-z – reference: Collins E, Augenstein I, Riedel S (2017) A supervised approach to extractive summarisation of scientific papers. In: Proceedings of the 21st conference on computational natural language learning (CoNLL 2017), pp 195–205 – reference: Page L, Brin S, Motwani R et al (1999) The pagerank citation ranking: Bringing order to the web. Tech. rep, Stanford InfoLab – reference: MallickCDasAKDingWEnsemble summarization of bio-medical articles integrating clustering and multi-objective evolutionary algorithmsAppl Soft Comput2021106107347 – reference: Saravanan M, Ravindran B, Raman S (2006) Improving legal document summarization using graphical models. In: Proceedings of the 2006 conference on legal knowledge and information systems: JURIX 2006: the nineteenth annual conference. IOS Press, NLD, pp 51–60 – reference: DuttaSChandraVMehraKEnsemble algorithms for microblog summarizationIEEE Intell Syst201833341410.1109/MIS.2018.033001411 – reference: MaslovSRednerSPromise and pitfalls of extending google’s pagerank algorithm to citation networksJ Neurosci2008284411,10311,1051:CAS:528:DC%2BD1cXhtlGltrbN10.1523/JNEUROSCI.0002-08.2008 – reference: MohammadiMRezaeiJEnsemble ranking: aggregation of rankings produced by different multi-criteria decision-making methodsOmega202096102254 – ident: 9349_CR3 doi: 10.1007/978-3-030-15712-8_27 – ident: 9349_CR5 doi: 10.18653/v1/K17-1021 – ident: 9349_CR27 – ident: 9349_CR33 doi: 10.1145/3462757.3466098 – ident: 9349_CR28 doi: 10.1145/2623330.2623732 – ident: 9349_CR31 – volume: 106 start-page: 347 issue: 107 year: 2021 ident: 9349_CR20 publication-title: Appl Soft Comput – ident: 9349_CR2 – ident: 9349_CR19 – volume: 96 start-page: 254 issue: 102 year: 2020 ident: 9349_CR24 publication-title: Omega – volume: 14 start-page: 241 year: 2019 ident: 9349_CR7 publication-title: Front Comp Sci doi: 10.1007/s11704-019-8208-z – ident: 9349_CR35 doi: 10.1145/3322640.3326728 – ident: 9349_CR4 doi: 10.1145/3462757.3466092 – volume: 41 start-page: 75 year: 2005 ident: 9349_CR34 publication-title: Inf Process Manage doi: 10.1016/j.ipm.2004.04.003 – volume: 54 start-page: 145 issue: 2 year: 2018 ident: 9349_CR22 publication-title: Inf Process Manage doi: 10.1016/j.ipm.2017.11.002 – ident: 9349_CR30 doi: 10.1109/IDEA49133.2020.9170675 – ident: 9349_CR16 – ident: 9349_CR25 doi: 10.1609/aaai.v31i1.10958 – volume: 33 start-page: 4 issue: 3 year: 2018 ident: 9349_CR8 publication-title: IEEE Intell Syst doi: 10.1109/MIS.2018.033001411 – volume: 22 start-page: 457 year: 2004 ident: 9349_CR9 publication-title: J Artif Intell Res doi: 10.1613/jair.1523 – ident: 9349_CR6 doi: 10.3233/FAIA210322 – volume: 28 start-page: 11,103 issue: 44 year: 2008 ident: 9349_CR21 publication-title: J Neurosci doi: 10.1523/JNEUROSCI.0002-08.2008 – ident: 9349_CR12 doi: 10.1145/2939672.2939754 – ident: 9349_CR26 doi: 10.1561/9781601984715 – ident: 9349_CR32 – ident: 9349_CR18 doi: 10.1145/3322640.3326715 – ident: 9349_CR11 – ident: 9349_CR15 doi: 10.18653/v1/D18-1449 – ident: 9349_CR17 – volume: 9 start-page: 391 year: 2021 ident: 9349_CR10 publication-title: Trans Assoc Comput Linguist doi: 10.1162/tacl_a_00373 – ident: 9349_CR1 doi: 10.1109/ICMLC.2015.7340924 – ident: 9349_CR13 – volume: 31 start-page: 5 year: 1999 ident: 9349_CR14 publication-title: ACM Comput Surv (CSUR) doi: 10.1145/345966.345982 – ident: 9349_CR23 doi: 10.1109/ICCES.2012.6408498 – ident: 9349_CR29 |
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