skip to main content
10.1145/3411763.3443441acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
extended-abstract

Towards Explainable AI: Assessing the Usefulness and Impact of Added Explainability Features in Legal Document Summarization

Published: 08 May 2021 Publication History

Abstract

This study tested two different approaches for adding an explainability feature to the implementation of a legal text summarization solution based on a Deep Learning (DL) model. Both approaches aimed to show the reviewers where the summary originated from by highlighting portions of the source text document. The participants had to review summaries generated by the DL model with two different types of text highlights and with no highlights at all. The study found that participants were significantly faster in completing the task with highlights based on attention scores from the DL model, but not with highlights based on a source attribution method, a model-agnostic formula that compares the source text and summary to identify overlapping language. The participants also reported increased trust in the DL model and expressed a preference for the attention highlights over the other type of highlights. This is because the attention highlights had more use cases, for example, the participants were able to use them to enrich the machine-generated summary. The findings of this study provide insights into the benefits and the challenges of selecting suitable mechanisms to provide explainability for DL models in the summarization task.

Supplemental Material

ZIP File
Supplemental material

References

[1]
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier del Ser, 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion 58: 82–115. arXiv:1910.10045. Retrieved from: https://arxiv.org/abs/1910.10045
[2]
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural Machine Translation by Jointly Learning to Align and Translate. arXiv:1409.0473. Retrieved from: https://arxiv.org/abs/1409.0473
[3]
Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2: 77–101.
[4]
John Brooke. SUS - A quick and dirty usability scale. Retrieved 10 May, 2020 from https://hell.meiert.org/core/pdf/sus.pdf
[5]
Bryce Goodman and Seth Flaxman. 2016. European Union regulations on algorithmic decision-making and a “right to explanation.”
[6]
Anna Jobin, Marcello Ienca, and Effy Vayena. 2019. The global landscape of AI ethics guidelines. Nature Machine Intelligence 1, 9: 389–399.
[7]
Guillaume Klein, Yoon Kim, Yuntian Deng, 2018. OpenNMT: Neural Machine Translation Toolkit. Proceedings of AMTA 2018, vol. 1, 177-184. Retrieved September 1, 2020 from: https://www.aclweb.org/anthology/W18-1817.pdf
[8]
Ramesh Nallapati, Bowen Zhou, Cicero Nogueira dos Santos, Caglar Gulcehre, and Bing Xiang. 2016. Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond. arXiv:1602.06023. Retrieved from: https://arxiv.org/abs/1602.06023
[9]
Romain Paulus, Caiming Xiong, and Richard Socher. 2017. A Deep Reinforced Model for Abstractive Summarization. arXiv:1705.04304. Retrieved from: https://arxiv.org/abs/1705.04304
[10]
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. “Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, 1135–1144.
[11]
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017. Get To The Point: Summarization with Pointer-Generator Networks. arXiv:1704.04368. Retrieved from: https://arxiv.org/abs/1704.04368
[12]
Abigail See. 2017. Taming Recurrent Neural Networks for Better Summarization. Retrieved September 9, 2020 from http://www.abigailsee.com/2017/04/16/taming-rnns-for-better-summarization.html
[13]
Thomson Reuters. Artificial Intelligence at Thomson Reuters. Retrieved September 20, 2020 from https://www.thomsonreuters.com/en/artificial-intelligence/introduction-to-artificial-intelligence-at-thomson-reuters.html
[14]
Matt Turek. 2020. Explainable Artificial Intelligence (XAI). Defense Advanced Research Projects Agency (DARPA). Retrieved September 20, 2020 from http://www.darpa.mil/program/explainable-artificial-intelligence

Cited By

View all
  • (2024)Do models explain themselves?Proceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692380(7880-7904)Online publication date: 21-Jul-2024
  • (2024)MetaWriter: Exploring the Potential and Perils of AI Writing Support in Scientific Peer ReviewProceedings of the ACM on Human-Computer Interaction10.1145/36373718:CSCW1(1-32)Online publication date: 26-Apr-2024
  • (2024)Understanding Omitted Facts in Transformer-based Abstractive Summarization2024 Moratuwa Engineering Research Conference (MERCon)10.1109/MERCon63886.2024.10688628(624-629)Online publication date: 8-Aug-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
May 2021
2965 pages
ISBN:9781450380959
DOI:10.1145/3411763
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2021

Check for updates

Qualifiers

  • Extended-abstract
  • Research
  • Refereed limited

Conference

CHI '21
Sponsor:

Acceptance Rates

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

Upcoming Conference

CHI 2025
ACM CHI Conference on Human Factors in Computing Systems
April 26 - May 1, 2025
Yokohama , Japan

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)175
  • Downloads (Last 6 weeks)13
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Do models explain themselves?Proceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692380(7880-7904)Online publication date: 21-Jul-2024
  • (2024)MetaWriter: Exploring the Potential and Perils of AI Writing Support in Scientific Peer ReviewProceedings of the ACM on Human-Computer Interaction10.1145/36373718:CSCW1(1-32)Online publication date: 26-Apr-2024
  • (2024)Understanding Omitted Facts in Transformer-based Abstractive Summarization2024 Moratuwa Engineering Research Conference (MERCon)10.1109/MERCon63886.2024.10688628(624-629)Online publication date: 8-Aug-2024
  • (2024)Unlocking Potential of Deep Learning: A Comprehensive Analysis of Legal Case Document Summarization2024 9th International Conference on Communication and Electronics Systems (ICCES)10.1109/ICCES63552.2024.10860267(1841-1849)Online publication date: 16-Dec-2024
  • (2024)Comparative Analysis of Legal Outcome Prediction with Detailed and Summarized Text2024 IEEE 9th International Conference for Convergence in Technology (I2CT)10.1109/I2CT61223.2024.10544203(1-6)Online publication date: 5-Apr-2024
  • (2024)Legal Natural Language Processing From 2015 to 2022: A Comprehensive Systematic Mapping Study of Advances and ApplicationsIEEE Access10.1109/ACCESS.2023.333394612(145286-145317)Online publication date: 2024
  • (2024)Abstractive text summarization: State of the art, challenges, and improvementsNeurocomputing10.1016/j.neucom.2024.128255603(128255)Online publication date: Oct-2024
  • (2024)Predicting Judgement Outcomes from Legal Case File Summaries with Explainable ApproachPattern Recognition10.1007/978-3-031-78107-0_11(167-183)Online publication date: 2-Dec-2024
  • (2024)HPSegNet: A Method for Handwritten and Printed Text Separation in Document ImagesDocument Analysis and Recognition – ICDAR 2024 Workshops10.1007/978-3-031-70642-4_12(184-198)Online publication date: 11-Sep-2024
  • (2024)Blockchain for Ethical and Transparent Generative AI Utilization by Banking and Finance LawyersExplainable Artificial Intelligence10.1007/978-3-031-63800-8_16(319-333)Online publication date: 10-Jul-2024
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media