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README.md

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----
-language:
-- en
-license:
-- mit
-task_categories:
-- question-answering
-- summarization
-- text-generation
-task_ids:
-- multiple-choice-qa
-- natural-language-inference
-configs:
-- gov_report
-- summ_screen_fd
-- qmsum
-- qasper
-- narrative_qa
-- quality
-- contract_nli
-- squad
-- squad_shuffled_distractors
-- squad_ordered_distractors
-- hotpotqa
-- hotpotqa_second_only
-tags:
-- multi-hop-question-answering
-- query-based-summarization
-- long-texts
----
-
-## Dataset Description
-- **Repository:** [SLED Github repository](https://github.com/Mivg/SLED)
-- **Paper:** [Efficient Long-Text Understanding with Short-Text Models
-](https://arxiv.org/pdf/2208.00748.pdf)
-
-# Dataset Card for SCROLLS
-
-## Overview
-This dataset is based on the [SCROLLS](https://huggingface.co/datasets/tau/scrolls) dataset ([paper](https://arxiv.org/pdf/2201.03533.pdf)), the [SQuAD 1.1](https://huggingface.co/datasets/squad) dataset and the [HotpotQA](https://huggingface.co/datasets/hotpot_qa) dataset.
-It doesn't contain any unpblished data, but includes the configuration needed for the [Efficient Long-Text Understanding with Short-Text Models
-](https://arxiv.org/pdf/2208.00748.pdf) paper.
-
-## Tasks
-The tasks included are:
-
-#### GovReport ([Huang et al., 2021](https://arxiv.org/pdf/2104.02112.pdf))
-GovReport is a summarization dataset of reports addressing various national policy issues published by the 
-Congressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary.
-The reports and their summaries are longer than their equivalents in other popular long-document summarization datasets; 
-for example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively.
-
-#### SummScreenFD ([Chen  et al., 2021](https://arxiv.org/pdf/2104.07091.pdf))
-SummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones).
-Given a transcript of a specific episode, the goal is to produce the episode's recap.
-The original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts. 
-For SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows, 
-making it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows. 
-Community-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze.
-
-#### QMSum ([Zhong et al., 2021](https://arxiv.org/pdf/2104.05938.pdf))
-QMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains. 
-The corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control, 
-and committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues.
-Annotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions, 
-while ensuring that the relevant text for answering each query spans at least 200 words or 10 turns.
-
-#### NarrativeQA ([Kočiský et al., 2021](https://arxiv.org/pdf/1712.07040.pdf))
-NarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites.
-Annotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs, 
-resulting in about 30 questions and answers for each of the 1,567 books and scripts.
-They were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast.
-Each question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical).
-
-#### Qasper ([Dasigi et al., 2021](https://arxiv.org/pdf/2105.03011.pdf))
-Qasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC).
-Questions were written by NLP practitioners after reading only the title and abstract of the papers, 
-while another set of NLP practitioners annotated the answers given the entire document.
-Qasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones.
-
-#### QuALITY ([Pang et al., 2021](https://arxiv.org/pdf/2112.08608.pdf))
-QuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg, 
-the Open American National Corpus, and more.
-Experienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them, 
-human annotators must read large portions of the given document. 
-Reference answers were then calculated using the majority vote between of the annotators and writer's answers.
-To measure the difficulty of their questions, Pang et al. conducted a speed validation process, 
-where another set of annotators were asked to answer questions given only a short period of time to skim through the document.
-As a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer.
-
-#### ContractNLI ([Koreeda and Manning, 2021](https://arxiv.org/pdf/2110.01799.pdf))
-Contract NLI is a natural language inference dataset in the legal domain.
-Given a non-disclosure agreement (the premise), the task is to predict whether a particular legal statement (the hypothesis) is entailed, not entailed (neutral), or cannot be entailed (contradiction) from the contract.
-The NDAs were manually picked after simple filtering from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) and Google.
-The dataset contains a total of 607 contracts and 17 unique hypotheses, which were combined to produce the dataset's 10,319 examples.
-
-#### SQuAD 1.1 ([Rajpurkar et al., 2016](https://arxiv.org/pdf/1606.05250.pdf)) 
-Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
-dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
-articles, where the answer to every question is a segment of text, or span, \
-from the corresponding reading passage, or the question might be unanswerable.
-
-#### HotpotQA ([Yang et al., 2018](https://arxiv.org/pdf/1809.09600.pdf))  
-HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features:
-(1) the questions require finding and reasoning over multiple supporting documents to answer;
-(2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas;
-(3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervisionand explain the predictions;
-(4) we offer a new type of factoid comparison questions to testQA systems’ ability to extract relevant facts and perform necessary comparison.
-
-## Data Fields
-
-All the datasets in the benchmark are in the same input-output format
-
-- `input`: a `string` feature. The input document.
-- `input_prefix`: an optional `string` feature, for the datasets containing prefix (e.g. question)
-- `output`: a `string` feature. The target.
-- `id`: a `string` feature. Unique per input.
-- `pid`: a `string` feature. Unique per input-output pair (can differ from 'id' in NarrativeQA and Qasper, where there is more then one valid target).
-
-The dataset that contain `input_prefix` are:
-- SQuAD - the question
-- HotpotQA - the question
-- qmsum - the query
-- qasper  - the question
-- narrative_qa - the question
-- quality - the question + the four choices
-- contract_nli - the hypothesis
-
-## Controlled experiments
-To test multiple properties of SLED, we modify SQuAD 1.1 [Rajpurkar et al., 2016](https://arxiv.org/pdf/1606.05250.pdf) 
-and HotpotQA [Yang et al., 2018](https://arxiv.org/pdf/1809.09600.pdf) to create a few controlled experiments settings.
-Those are accessible via the following configurations:
-- squad - Contains the original version of SQuAD 1.1 (question + passage)
-- squad_ordered_distractors - For each example, 9 random distrctor passages are concatenated (separated by '\n')
-- squad_shuffled_distractors - For each example, 9 random distrctor passages are added (separated by '\n'), and jointly the 10 passages are randomly shuffled
-- hotpotqa - A clean version of HotpotQA, where each input contains only the two gold passages (separated by '\n')
-- hotpotqa_second_only - In each example, the input contains only the second gold passage
-
-## Citation
-If you use this dataset, **please make sure to cite all the original dataset papers as well SCROLLS.** [[bibtex](https://drive.google.com/uc?export=download&id=1IUYIzQD9DPsECw0JWkwk4Ildn8JOMtuU)]
-```
-@inproceedings{Ivgi2022EfficientLU,
-  title={Efficient Long-Text Understanding with Short-Text Models},
-  author={Maor Ivgi and Uri Shaham and Jonathan Berant},
-  year={2022}
-}
-```

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