Create README.md

README.md

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+---
+language:
+- en
+task_categories:
+- question-answering
+- summarization
+- text-generation
+task_ids:
+- multiple-choice-qa
+- natural-language-inference
+configs:
+- gov_report
+- summ_screen_fd
+- qmsum
+- squality
+- qasper
+- narrative_qa
+- quality
+- musique
+- space_digest
+- book_sum_sort
+tags:
+- query-based-summarization
+- long-texts
+---
+
+## Dataset Description
+
+- **Homepage:** [ZeroSCROLLS](https://www.zero.scrolls-benchmark.com/)
+- **Leaderboard:** [Leaderboard](https://www.zero.scrolls-benchmark.com/leaderboard)
+- **Point of Contact:** [[email protected]]([email protected])
+
+# Dataset Card for ZeroSCROLLS
+
+## Overview
+ZeroSCROLLS zero-shot benchmark for natural language understanding over long texts.
+
+## Leaderboard
+The ZeroSCROLLS benchmark leaderboard can be found [here](https://www.zero.scrolls-benchmark.com/leaderboard).
+
+## Tasks
+ZeroSCROLLS comprises the following tasks:
+
+#### 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., 2022](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.
+
+#### SQuALITY ([Wang  et al., 2022](https://arxiv.org/pdf/2205.11465.pdf))
+SQuALITY (Wang et al., 2022) is a question-focused summarization dataset, where given a story from Project Gutenberg, 
+the task is to produce a summary of the story or aspects of it based on a guiding question.
+The questions and summaries are original and crowdsourced; experienced writers were guided to design questions that require reading significant parts of the story to answer correctly.
+
+
+#### 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.
+
+#### NarrativeQA ([Kočiský et al., 2018](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).
+
+#### QuALITY ([Pang et al., 2022](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.
+
+#### MuSiQue ([Trivedi et al., 2022](https://arxiv.org/pdf/2108.00573.pdf))
+MuSiQue is a multi-hop question answering dataset, where the inputs are 20 Wikipedia paragraphs and a question that requires multiple hops between different paragraphs.
+In the original dataset, each question also has an unanswerable twin question, where the correct answer is not present in the paragraphs.
+
+#### SpaceDigest (New)
+SpaceDigest is a new sentiment aggregation task. Given 50 hotel reviews (without their ratings) from the Space dataset (Angelidis et al., 2021), the task is to determine the percentage of positive reviews.
+
+#### BookSumSort (New)
+BookSumSort is a new task based on the BookSum dataset (Kry ́sci ́nski et al., 2022), which contains summaries of chapters (or parts) of novels, plays, and long poems from various sources. 
+Given a shuffled list of chapter summaries, the task is to reorder them according to the original order of summaries in BookSum.
+
+## Data Fields
+
+Most datasets in the benchmark are in the same input-output format
+
+- `input`: a `string` feature. The input document.
+- `output`: this feature is always None, as ZeroSCROLLS contains only test sets.
+- `id`: a `string` feature. Unique per input.
+- `pid`: a `string` feature, identical to 'id`. Facilitates evaluating tasks with multiple refrences per input.
+- `document_start_index`: an `int32` feature. Character index that enables easy parsing of the context document.
+- `document_end_index`: an `int32` feature. Character index that enables easy parsing of the context document.
+- `query_start_index`: an `int32` feature. Character index that enables easy parsing of the query, if exists.
+- `query_end_index`: an `int32` feature. Character index that enables easy parsing of the query, if exists.
+- `truncation_seperator`: a `string` feature. The string used to append to a trimmed context document, mentioning the context was trimmed.
+
+Datasets containing multiple documents inside the `input` feature are MuSiQue, SpaceDigest, and BookSumSort. They also have the following feature:
+
+- `inner_docs_start_indices`: a sequence of `int32` feature. Character indexes that enables easy parsing of the the inner documents, e.g. Reviews, of Summaries.
+
+
+
+## Citation
+If you use the ZeroSCROLLS data, **please make sure to cite all of the original dataset papers.** [[bibtex](https://zero-scrolls-tau.s3.us-east-2.amazonaws.com/zero_scrolls_datasets.bib)]
+```
+@inproceedings{}
+```