Automatic summarization of legal case documents is an important and practical challenge.
Moreover, standard ROUGE evaluation metrics are unable to quantify the perceived (un)fairness of the summaries.
To enable tasks such as search/retrieval and classification over all the available data, we need robust algorithms for text normalization, i. e., for cleaning different kinds of noise in the text.
Most existing algorithms for cross-modal Information Retrieval are based on a supervised train-test setup, where a model learns to align the mode of the query (e. g., text) to the mode of the documents (e. g., images) from a given training set.
The few prior works that attempted matching only considered the resources, and no attempt has been made to understand other aspects of needs/availabilities that are essential for matching in practice.
We propose to augment the PCNet with the hierarchy of legal statutes, to form a heterogeneous network Hier-SPCNet, having citation links between case documents and statutes, as well as citation and hierarchy links among the statutes.
Although a lot of research has been done on utilising Online Social Media during disasters, there exists no system for a specific task that is critical in a post-disaster scenario -- identifying resource-needs and resource-availabilities in the disaster-affected region, coupled with their subsequent matching.
Computing similarity between two legal documents is an important and challenging task in the domain of Legal Information Retrieval.
Automatically understanding the rhetorical roles of sentences in a legal case judgement is an important problem to solve, since it can help in several downstream tasks like summarization of legal judgments, legal search, and so on.
Specifically, considering that an extractive summarization algorithm selects a subset of the textual units (e. g. microblogs) in the original data for inclusion in the summary, we investigate whether this selection is fair or not.
The proposed method is superior to the state-of-the-art method not only for IR evaluation measures but also in terms of time requirements.