license: cc-by-4.0
task_categories:
- text-retrieval
- summarization
language:
- en
tags:
- legal
- law
size_categories:
- n<1K
source_datasets:
- launch/gov_reports
dataset_info:
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: test
num_examples: 973
- config_name: corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_examples: 973
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_examples: 970
configs:
- config_name: default
data_files:
- split: test
path: data/default.jsonl
- config_name: corpus
data_files:
- split: corpus
path: data/corpus.jsonl
- config_name: queries
data_files:
- split: queries
path: data/queries.jsonl
pretty_name: GovReport (MTEB format)
GovReport (MTEB format)
This is the test split of the GovReport dataset formatted in the Massive Text Embedding Benchmark (MTEB) information retrieval dataset format.
This dataset is intended to facilitate the consistent and reproducible evaluation of information retrieval models on GovReport with the mteb embedding model evaluation framework.
More specifically, this dataset tests the ability of information retrieval models to retrieve US government reports from their summaries.
This dataset has been processed into the MTEB format by Isaacus, a legal AI research company.
Methodology 🧪
To understand how GovReport was created, refer to its creators' paper.
This dataset was formatted by treating the summary column of GovReport as queries (or anchors) and the document column as relevant (or positive) passages.
Structure 🗂️
As per the MTEB information retrieval dataset format, this dataset comprises three splits, default, corpus and queries.
The default split pairs summaries (query-id, linked to the _id column of the queries split) with government reports (corpus-id, linked to the _id column of the corpus split), each pair having a score of 1.
The corpus split contains government reports, with the text of a report being stored in the text key and its id being stored in the _id key.
The queries split contains summaries, with the text of a summary being stored in the text key and its id being stored in the _id key.
License 📜
This dataset is licensed under CC BY 4.0.
Citation 🔖
@inproceedings{huang-etal-2021-efficient,
title = "Efficient Attentions for Long Document Summarization",
author = "Huang, Luyang and
Cao, Shuyang and
Parulian, Nikolaus and
Ji, Heng and
Wang, Lu",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.112",
doi = "10.18653/v1/2021.naacl-main.112",
pages = "1419--1436",
abstract = "The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization. In this paper, we propose Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. We further conduct a systematic study of existing efficient self-attentions. Combined with Hepos, we are able to process ten times more tokens than existing models that use full attentions. For evaluation, we present a new dataset, GovReport, with significantly longer documents and summaries. Results show that our models produce significantly higher ROUGE scores than competitive comparisons, including new state-of-the-art results on PubMed. Human evaluation also shows that our models generate more informative summaries with fewer unfaithful errors.",
eprint={2104.02112}
}