# Encoded Summarization: Summarizing Documents into Continuous Vector Space for Legal Case Retrieval

arXiv: https://arxiv.org/abs/2309.08187 · HTML: https://mlsanity.com/paper/2309.08187/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Enhanced LSTM for Natural Language Inference](https://mlsanity.com/paper/1609.06038/) (2016)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1058/) (2014)
- [Automatic Catchphrase Extraction from Legal Case Documents via Scoring using Deep Neural Networks](https://mlsanity.com/paper/1809.05219/) (2018)

## Built on by (most cited first, 6 of 6)

- [Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation](https://mlsanity.com/paper/2406.19760/) (2024)
- [A Comprehensive Survey on Legal Summarization: Challenges and Future Directions](https://mlsanity.com/paper/2501.17830/) (2025)
- [Constructing a Knowledge Graph for Vietnamese Legal Cases with Heterogeneous Graphs](https://mlsanity.com/paper/2309.09069/) (2023)
- [Responsible AI Considerations in Text Summarization Research: A Review of Current Practices](https://mlsanity.com/paper/2311.11103/) (2023)
- [LLM-based Embedders for Prior Case Retrieval](https://mlsanity.com/paper/2507.18455/) (2025)
- [DALDALL: Data Augmentation for Lexical and Semantic Diverse in Legal Domain by leveraging LLM-Persona](https://mlsanity.com/paper/2603.22765/) (2026)

## Abstract (arXiv)

We present our method for tackling a legal case retrieval task by introducing our method of encoding documents by summarizing them into continuous vector space via our phrase scoring framework utilizing deep neural networks. On the other hand, we explore the benefits from combining lexical features and latent features generated with neural networks. Our experiments show that lexical features and latent features generated with neural networks complement each other to improve the retrieval system performance. Furthermore, our experimental results suggest the importance of case summarization in different aspects: using provided summaries and performing encoded summarization. Our approach achieved F1 of 65.6% and 57.6% on the experimental datasets of legal case retrieval tasks.