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A sentence is known by the company it keeps: Improving Legal Document Summarization Using Deep Clustering

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The appropriate understanding and fast processing of lengthy legal documents are computationally challenging problems. Designing efficient automatic summarization techniques can potentially be the key to deal with such issues. Extractive summarization is one of the most popular approaches for forming summaries out of such lengthy documents, via the process of summary-relevant sentence selection. An efficient application of this approach involves appropriate scoring of sentences, which helps in the identification of more informative and essential sentences from the document. In this work, a novel sentence scoring approach DCESumm is proposed which consists of supervised sentence-level summary relevance prediction, as well as unsupervised clustering-based document-level score enhancement. Experimental results on two legal document summarization datasets, BillSum and Forum of Information Retrieval Evaluation (FIRE), reveal that the proposed approach can achieve significant improvements over the current state-of-the-art approaches. More specifically it achieves ROUGE metric F1-score improvements of (1−6)% and (6−12)% for the BillSum and FIRE test sets respectively. Such impressive summarization results suggest the usefulness of the proposed approach in finding the gist of a lengthy legal document, thereby providing crucial assistance to legal practitioners.

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Appendix A. Qualitative analysis

Appendix A. Qualitative analysis

1.1 A.1 Best and worst sample predictions

Table 12 Sample Predicted summary of maximum ROUGE score from CA test
Table 13 Sample Predicted summary of minimum ROUGE score from CA test

1.2 A.2 Sample predicted summaries after postprocessing

Table 14 shows the scores obtained by the sample with the lowest ROUGE score among all the samples in the case of US Test data. More specifically, it shows the scores of top 15% sentences. These sentences are then sorted as they appear in the original document to form a summary. From these scores, we see that not all the scores are good to be included into the summary. Similar trend has been shown in the case of US Test and CA Test dataset as shown in Tables 15 and 16. Table 17 shows the predicted summaries of worst sample after postprocessing step. This shows that, postprocessing step can actually be very helpful to improve the quality of the predicted summaries further. Table 18 shows the ROUGE scores on those samples which has obtained the minimum ROUGE scores. From this table, we see that after postprocessing which includes picking the one ore two top scoring sentences helps in improving the quality of summary and hence ROUGE scores.

Table 14 Top 15% sentences from US Test data which has obtained the lowest ROUGE scores
Table 15 Top 15% sentences from CA Test data which has obtained the lowest ROUGE scores
Table 16 Top 40% sentences from FIRE Test data which has obtained the lowest ROUGE scores
Table 17 Postprocessing Summary sample for the worst Summary
Table 18 ROUGE scores with the proposed approach on the worst sample after postprocessing

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Jain, D., Borah, M.D. & Biswas, A. A sentence is known by the company it keeps: Improving Legal Document Summarization Using Deep Clustering. Artif Intell Law (2023).

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