# Building Legal Case Retrieval Systems with Lexical Matching and Summarization using A Pre-Trained Phrase Scoring Model

arXiv: https://arxiv.org/abs/2009.14083 · HTML: https://mlsanity.com/paper/2009.14083/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 24 of 24)

- [ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://mlsanity.com/paper/2105.13562/) (2021)
- [Semantic Segmentation of Legal Documents via Rhetorical Roles](https://mlsanity.com/paper/2112.01836/) (2021)
- [Corpus for Automatic Structuring of Legal Documents](https://mlsanity.com/paper/2201.13125/) (2022)
- [THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval](https://mlsanity.com/paper/2305.06812/) (2023)
- [U-CREAT: Unsupervised Case Retrieval using Events extrAcTion](https://mlsanity.com/paper/2307.05260/) (2023)
- [HLDC: Hindi Legal Documents Corpus](https://mlsanity.com/paper/2204.00806/) (2022)
- [Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks](https://mlsanity.com/paper/2301.12847/) (2023)
- [JNLP Team: Deep Learning Approaches for Legal Processing Tasks in COLIEE 2021](https://mlsanity.com/paper/2106.13405/) (2021)
- [MUSER: A Multi-View Similar Case Retrieval Dataset](https://mlsanity.com/paper/2310.15602/) (2023)
- [SemEval 2023 Task 6: LegalEval - Understanding Legal Texts](https://mlsanity.com/paper/2304.09548/) (2023)
- [An Intent Taxonomy of Legal Case Retrieval](https://mlsanity.com/paper/2307.13298/) (2023)
- [Optimizing Numerical Estimation and Operational Efficiency in the Legal Domain through Large Language Models](https://mlsanity.com/paper/2407.19041/) (2024)
- [LeCoPCR: Legal Concept-guided Prior Case Retrieval for European Court of Human Rights cases](https://mlsanity.com/paper/2501.14114/) (2025)
- [Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing](https://mlsanity.com/paper/2212.09125/) (2022)
- [LEEC: A Legal Element Extraction Dataset with an Extensive Domain-Specific Label System](https://mlsanity.com/paper/2310.01271/) (2023)
- [Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network](https://mlsanity.com/paper/2105.06083/) (2021)
- [Transformer-based Approaches for Legal Text Processing](https://mlsanity.com/paper/2202.06397/) (2022)
- [When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance](https://mlsanity.com/paper/2507.07748/) (2025)
- [Legal Element-oriented Modeling with Multi-view Contrastive Learning for Legal Case Retrieval](https://mlsanity.com/paper/2210.05188/) (2022)
- [Analyzing Hong Kong's Legal Judgments from a Computational Linguistics point-of-view](https://mlsanity.com/paper/2305.02558/) (2023)
- [Adversarially Robust Neural Legal Judgement Systems](https://mlsanity.com/paper/2308.00165/) (2023)
- [Towards Automatic Comparison of Data Privacy Documents: A Preliminary Experiment on GDPR-like Laws](https://mlsanity.com/paper/2105.10117/) (2021)
- [An interpretability framework for Similar case matching](https://mlsanity.com/paper/2304.01622/) (2023)
- [Knowledge Augmented Entity and Relation Extraction for Legal Documents with Hypergraph Neural Network](https://mlsanity.com/paper/2602.08289/) (2026)

## Abstract (arXiv)

We present our method for tackling the legal case retrieval task of the Competition on Legal Information Extraction/Entailment 2019. Our approach is based on the idea that summarization is important for retrieval. On one hand, we adopt a summarization based model called encoded summarization which encodes a given document into continuous vector space which embeds the summary properties of the document. We utilize the resource of COLIEE 2018 on which we train the document representation model. On the other hand, we extract lexical features on different parts of a given query and its candidates. We observe that by comparing different parts of the query and its candidates, we can achieve better performance. Furthermore, the combination of the lexical features with latent features by the summarization-based method achieves even better performance. We have achieved the state-of-the-art result for the task on the benchmark of the competition.