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Legal Document Summarization from classical approaches to State-of-the-art methods

This repository accompanying the code for my master's thesis LegSum: Legal Document Summarization

Notebook:

Abstractive Methods

Notebook Colab Model checkpoint
T5 Open In Colab Frederick0291/t5-small-finetuned-billsum
BART billsum Open In Colab murali-admin/bart-billsum-1
BART xsum Open In Colab sshleifer/distilbart-xsum-12-6
Pegasus Legal Open In Colab nsi319/legal-pegasus
Pegasus billsum Open In Colab google/pegasus-billsum
BigBird Open In Colab google/bigbird-pegasus-large-bigpatent
LED Open In Colab allenai/led-large-16384-arxiv

Extractive Methods

Notebook Colab
Extractive Open In Colab
Kmeans Bertsum Open In Colab
Luhn's algorithm Open In Colab
TF-IDF Open In Colab

DataSet:

  1. BillSum

Results:

Following results are on BillSum Dataset (ca_test) with pre-trained models and extractive methods

Algorithm / model Rouge-1 Rouge-2 Rouge-L
Extractive
KL 24.44 9.74 21.98
LSA 30.85 12.45 27.64
SumBasics 31.01 12.61 27.83
Bert 33.29 15.17 29.67
Tf-Idf 33.97 15.98 29.92
LexRank 36.83 18.98 32.95
TextRank 36.57 19.10 32.35
Luhn’s Algorithm 37.48 19.93 33.35
Abstractive
BART 26.02 11.87 22.02
Pegasus(small) 28.61 12.19 25.88
T5(small) 32.99 15.52 30.21
BillPegasus 34.25 16.63 30.22

Demo

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Legal Documents Summarization

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