Extract-Explain-Abstract: A Rhetorical Role-Driven Domain-Specific Summarisation Framework for I ndian Legal Documents (2025)

Abstract: Legal documents are characterized by theirlength, intricacy, and dense use of jargon, making efficacious summarisation both paramountand challenging. Existing zero-shot methodologies in small language models struggle tosimplify this jargon and are prone to punts andhallucinations with longer prompts. This paperintroduces the Rhetorical Role-based Extract-Explain-Abstract (EEA) Framework, a novelthree-stage methodology for summarisation ofIndian legal documents in low-resource settings. The approach begins by segmenting legaltexts using rhetorical roles, such as facts, issues and arguments, through a domain-specificphrase corpus and extraction based on TF-IDF.In the explanation stage, the segmented output is enriched with logical connections to ensure coherence and legal fidelity. The final abstraction phase condenses these interlinked segments into cogent, high-level summaries thatpreserve critical legal reasoning. Experimentson Indian legal datasets show that the EEAframework typically outperforms in ROUGE,BERTScore, Flesch Reading Ease, Age of Acquisition, SummaC and human evaluations. Wealso employ InLegalBERTScore as a metric tocapture domain specific semantics of Indianlegal documents.

## introduction lawyers spend up to 2.3 hours a week searching for documents and another 2 hours recreating missing ones, resulting in an annual loss of $9,071 per lawyer, or 9.8% of their productivity @xcite . here, summarisation plays a crucial role. text summarisation can be extractive; selecting key phrases verbatim, or abstractive; generating new text using nlp methods. despite advancements in both supervised and unsupervised approaches, accurately aligning summaries with legal standards remains difficult. we highlight the use of small language models (slms) which are preferred in low-resource settings with respect to availability of data, computational power, as finetuning can become resource heavy. this directly addresses the challenges of high resource demands and domain-specific accuracy in legal summarisation tasks @xcite . although, slms have limited reasoning capacity and are prone to hallucinations, text degenerations and punts @xcite . we address these challenges with a three-stage, zero-shot summarisation framework. first, rhetorical structure theory (rst) is applied to extract legally significant segments @xcite . second, a chain-of-thought (cot) prompt guides the model in interpreting these segments. third, an abstractive module generates a cogent summary. the remainder of this paper is structured as follows: section 2 reviews related work; section 3 describes our methodology; section 4 covers experimental setup and evaluation; section 5 presents results; section 6 delineates human evaluation. ## related work summarisation can be broadly categorised as extractive and abstractive. extractive approaches like @xcite and kmm @xcite ) rank sentences using tf-idf and k-mixture models. casesummarizer @xcite targets legal documents. abstractive methods include @xcite ; being the first of it's kind for legal documents. legal document understanding is effectively improved by incorporating sequential structural relationships @xcite . additionally, introducing cot methodology @xcite has proven to enhance the quality of summarisation @xcite . ## proposed model we propose a novel three-stage extract-explain-abstract (eea) framework( figure 4 in appendix a.4) for summarisation, extending extractthen-abstract methods @xcite . extraction selects relevant sentences, explanation organizes them logically, and abstraction generates a concise summary. using cot prompting, we replace element-aware extraction @xcite with rhetorical role-based extraction from a domain-specific corpus. we compare our methodology's results with existing methods such as extract-then-abstract (ea) and regular zero-shot summarisation (abstract), to show that our summaries performed better across automated metrics and are preferred by human experts as well. figure 1 : dependency flow across rhetorical roles: facts lead to issues, which frame arguments, supported by statutes and precedents, culminating in the court's rationale. ## results to evaluate the quality of generated summaries, we report rouge scores ( we also evaluate the readability and linguistic simplicity of the generated summaries ( ## conclusion this paper presents the extract-explain-abstract (eea), a novel three stage approach to improve factuality and readability in summarisation of indian legal documents. the "explain" stage of our framework enhances contextual precision, preserving necessary nuances while simplifying the jargon. our presented methodology can be adapted across any judiciary system as the rhetorical roles stay the same, providing grounds for future experimental work. moreover, eea generates the least number of punts outperforming both ea and abstractive summarisation methods with significant reduction in hallucination. this promises accurate and context-aware summarisation in resourceconstrained environments.