Legal Argument Extraction from Court Judgements using Integer Linear Programming (2023)

Abstract: Legal arguments are one of the key aspects of legal knowledge which are expressed in various ways in the unstructured text of court judgements. A large database of past legal arguments can be created by extracting arguments from court judgements, categorizing them, and storing them in a structured format. Such a database would be useful for suggesting suitable arguments for any new case. In this paper, we focus on extracting arguments from Indian Supreme Court judgements using minimal supervision. We first identify a set of certain sentence-level argument markers which are useful for argument extraction such as whether a sentence contains a claim or not, whether a sentence is argumentative in nature, whether two sentences are part of the same argument, etc. We then model the legal argument extraction problem as a text segmentation problem where we combine multiple weak evidences in the form of argument markers using Integer Linear Programming (ILP), finally arriving at a global document-level solution giving the most optimal legal arguments. We demonstrate the effectiveness of our technique by comparing it against several competent baselines.

## introduction in the field of argument mining, extraction of legal arguments from court judgements has been receiving increasing attention @xcite @xcite . most of these approaches are supervised in nature in the sense that they need a significantly large corpus of documents from a specific area (e.g., echr -european court of human rights) which are annotated with legal arguments. in this paper, we focus on extracting legal arguments from indian supreme court judgements using minimal supervision. our goal is to construct a large database of past legal arguments by extracting legal arguments from court judgements, categorizing them, and storing them in a structured format. such a database would be useful in building a high-level legal decision support system where some of its features could bei) suggesting suitable arguments given a new case description, ii) learning to estimate the strength of a new argument based on the similar past arguments that helped to win the case. in this paper, we focus specifically on extraction of legal arguments and to the best of our knowledge, this is the first such attempt for -i) legal argument extraction without any in-domain supervision and ii) argument extraction from indian court judgements. for categorizing the arguments, we propose to simply map them to the statute facets which were recently proposed in our previous work @xcite . a statute facet is any important specific aspect of an act which can be potentially used in legal arguments in a case related to the act. for example, following are statute facets from india's industrial disputes act -workman, illegal strikes, and notice of retrenchment. we consider a legal argument as a sequence of contiguous sentences in a court judgement which constitute a complete and coherent argument. a legal argument generally consists of a sentence containing a major claim (or conclusion) and other sentences acting as sufficient premises for that claim. a major challenge in legal argument extraction from indian court judgements is the unavailability of a training dataset where the legal arguments are annotated by human experts. hence, we first propose to identify certain argument markers within sentences of a court judgement which are weak indicators of presence of a legal argument. here, we refer to these argument markers as weak evidences because individually any marker is not a strong enough indicator of a legal argument and it is also not possible to automatically identify these arguments -there were different systems of dearness allowance for the operators and the clerical and subordinate staff in the appellant company. ## problem definition the problem is formally defined as follows: input: (i) a court judgement document j (sequence of n sentences s 1 , s 2 , • • • , s n ), and (ii) a set of statute facets f 1 , f 2 , • • • , f k for an act a output: a set of extracted arguments where any i th argument is a tuple (i s , i e ) such that all the contiguous sentences starting from s is to s ie constitute the argument. scope and assumptions: if there are multiple arguments present in j, they must be mutually exclusive, i.e., no sentence is common between any two such arguments. also, another simplifying assumption is that an argument consists of contiguous sentences which may not be always true foot_0 . extending our techniques to extract non-contiguous arguments is to be tackled as a part of future work. ## proposed techniques in this section, we describe identification of various argument markers and our proposed argument extraction techniques which use these markers. ## conclusions and future work we proposed a technique to extract legal arguments from indian supreme court judgements by first identifying a set of certain argument markers and then incorporating them in an integer linear programming (ilp) framework with domain knowledge based constraints. individually, these argument markers are weak indicators of arguments mentioned in the text of a judgement, but the information from multiple such markers gets combined effectively in our argext-ilp technique. we annotated a small dataset of 10 court judgements containing 127 legal arguments and evaluated our techniques on it along with multiple competent baselines. we demonstrated that argext-ilp outperforms other baselines across multiple evaluation metrics. to the best of our knowledge, this is the first attempt to extract legal arguments from indian court judgements and also a first argumentsannotated dataset for the same. as part of future work, our argument extraction techniques need to be improved further in multiple aspects -(i) the accuracy of identifying individual argument markers needs to be improved further which will automatically improve argext-ilp's performance, (ii) we plan to do away with some of our simplifying assumptions to also extract overlapping and noncontiguous arguments, and (iii) we plan to evaluate our techniques on a wider variety of court judgements such as judgements other than industrial disputes and also from other geographies than india. list of verbs used in lr3: sustain, contend, argue, debate, assert, conclude, assess, believe, maintain, submit, show, demonstrate, prove, appear, seem, clear, justify, conclude, claim, affirm, arrogate, indicate, clarify, hold, opine. also note that the list contains only the base forms of these verbs but while matching in the sentence, we consider all the morphological variations such as conclude ⇒ concluded, concluding, concludes. list of negative adjectives used in lr4: unfair, erroneous, incorrect, wrong, inaccurate, inexact, imprecise, invalid, fallacious, misleading, illogical, unsound, faulty, flawed, spurious, unfounded, unjustified, illegal, inappropriate, inconsistent, unsustainable, unwarranted list of positive adjectives used in lr4: correct, accurate, exact, precise, valid, logical, justified, warranted, consistent, sustained, fair, legal, appropriate, permitted, maintainable. list of negative adverbs used in lr4: inconsistently, unfairly, erroneously, incorrectly, wrongly, mistakenly, illegally, inappropriately, spuriously. list of positive adverbs used in lr4: consistently, fairly, correctly, legally, appropriately.