CivilSum: A dataset for abstractive summarization of Indian court decisions

Extracting relevant information from legal documents is a challenging task due to the technical complexity and volume of their content. These factors also increase the costs of annotating large datasets, which are required to train state-of-the-art summarization systems. To address these challenges, we introduce CivilSum, a collection of 23,350 legal case decisions from the Supreme Court of India and other Indian High Courts paired with human-written summaries. Compared to previous datasets such as IN-Abs, CivilSum not only has more legal decisions but also provides shorter and more abstractive summaries, thus offering a challenging benchmark for legal summarization. Unlike other domains such as news articles, our analysis shows the most important content tends to appear at the end of the documents. We measure the effect of this tail bias on summarization performance using strong architectures for long-document abstractive summarization, and the results highlight the importance of long sequence modeling for the proposed task. CivilSum and related code are publicly available to the research community to advance text summarization in the legal domain.