CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization (2025.findings-naacl)
Copied to clipboard
| Challenge: | Recent efforts to produce concise legal summarization have shifted towards abstractive approaches . |
| Approach: | They propose a framework that integrates content selection and planning components to generate coherent summaries based on both the content and the structured plan. |
| Outcome: | The proposed framework shows that it integrates content selection and planning components over entity-centric approaches in the context of legal judgements. |
Similar Papers
LexAbSumm: Aspect-based Summarization of Legal Decisions (2024.lrec-main)
Copied to clipboard
| Challenge: | LexAbSumm is a dataset designed for aspect-based summarization of legal documents . it is based on a set of ECtHR fact sheets, and is available for download. |
| Approach: | They propose a dataset designed for aspect-based summarization of legal case decisions . they evaluate abstractive summarizing models tailored for longer documents . |
| Outcome: | The proposed dataset is designed for aspect-based summarization of legal cases . it reveals a challenge in conditioning models to produce aspect-specific summaries . |
An Evaluation Framework for Legal Document Summarization (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing metrics for summarizing legal documents fail to evaluate intent in the original text. |
| Approach: | They propose an automated intent-based summarization metric which shows a better agreement with human evaluation as compared to other automated metrics like BLEU, ROUGE-L etc. |
| Outcome: | The proposed method shows that human evaluation is more accurate than other metrics. |
RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity (2025.findings-naacl)
Copied to clipboard
| Challenge: | Current approaches to legal summarization struggle with content theme deviation and inconsistent writing styles due to the content of the source document. |
| Approach: | They propose a retrieval-augmented framework that utilizes exemplar summaries along with the source document to guide the model. |
| Outcome: | The proposed model outperforms models that do not utilize exemplars and those that rely on similarity-based exemplar selection. |
Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation (2022.aacl-main)
Copied to clipboard
Abhay Shukla, Paheli Bhattacharya, Soham Poddar, Rajdeep Mukherjee, Kripabandhu Ghosh, Pawan Goyal, Saptarshi Ghosh
| Challenge: | Summarization of legal case judgement documents is a challenging problem in Legal NLP. |
| Approach: | They propose to use extractive and abstractive summarization methods to evaluate legal document summarizing systems. |
| Outcome: | The proposed methods have been evaluated on three legal summarization datasets. |
LexKeyPlan: Planning with Keyphrases and Retrieval Augmentation for Legal Text Generation: A Case Study on European Court of Human Rights Cases (2025.acl-short)
Copied to clipboard
| Challenge: | Large language models excel at text generation but often produce hallucinations due to their sole reliance on parametric knowledge. |
| Approach: | They propose a framework that integrates anticipatory planning into legal text generation by generating keyphrases outlining future content serving as forward-looking plan. |
| Outcome: | The proposed framework improves factual accuracy and coherence by retrieving information aligned with the intended content. |
Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Summarizing legal decisions requires the expertise of law practitioners, which is time- and cost-intensive. |
| Approach: | They propose methods for extracting summarized legal decisions using limited expert annotated data. |
| Outcome: | The proposed models achieve ROUGE scores vis-à-vis expert extracted summaries that match inter-annotator comparisons. |
Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization (2024.naacl-long)
Copied to clipboard
| Challenge: | a study explores the cross-jurisdictional generalizability of legal case summarization models . fine-tuning on non-target datasets outperforms unsupervised methods, but success depends on similarity between source and target jurisdictions. |
| Approach: | They explore how to effectively summarize legal cases of a target jurisdiction where reference summaries are not available. |
| Outcome: | The proposed model can be generalized across jurisdictions and improve transfer performance. |
Event-Keyed Summarization (2024.findings-emnlp)
Copied to clipboard
| Challenge: | a novel task combines document-level event extraction with event-keyed summarization . a recent study has shown that traditional summarizing produces inferior summaries of target events . |
| Approach: | They propose a task that marries traditional summarization and document-level event extraction with the goal of generating a contextualized summary for a specific event, given a document and an extracted event structure. |
| Outcome: | The proposed task combines document-level event extraction with event-keyed summarization . the authors show that the proposed task is robust and humane . |
EntSUM: A Data Set for Entity-Centric Extractive Summarization (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for controllable summarization fail to generate entity-centric summaries. |
| Approach: | They propose to use a human-annotated data set EntSUM to generate controllable summarization with a focus on named entities as the aspects to control. |
| Outcome: | The proposed data set shows that existing methods fail to generate entity-centric summaries. |
Content Planning for Neural Story Generation with Aristotelian Rescoring (2020.emnlp-main)
Copied to clipboard
| Challenge: | Current approaches to narrative composition are plagued by difficulty in mastering structure, will veer between topics, and lack long-range cohesion. |
| Approach: | They propose a plot-generation language model and a set of rescoring models that implement an aspect of good story-writing as detailed in Aristotle's Poetics. |
| Outcome: | The proposed system improves the quality of the narrative generated from the proposed model and improves its relevance to a given prompt and quality of stories written with our principled plot structure. |