Update scrolls.py

scrolls.py

@@ -1,9 +1,6 @@
 # coding=utf-8
-# TODO License
-
 # Lint as: python3
-"""The Scrolls benchmark."""
-
+"""The SCROLLS benchmark."""
 
 import json
 import os
@@ -12,47 +9,70 @@ import datasets
 _SCROLLS_CITATION = """
 @article{ TODO citation here
 }
-Note that each Scrolls dataset has its own citation. Please see the source to
+Note that each SCROLLS dataset has its own citation. Please see the source to
 get the correct citation for each contained dataset.
 """
 
 _SCROLLS_DESCRIPTION = """
-Scrolls (https://TODO.com/) is a benchmark of generative tasks that require processing long texts.
+SCROLLS: Standardized CompaRison Over Long Language Sequences.
+A suite of natural language datasets that require reasoning over long texts.
+https://scrolls-benchmark.com/
 """
 
 _SUMM_SCREEN_DESCRIPTION = """
-SummScreen (Boolean Questions, Clark et al., 2019a) is a summarization task where one should output the recap of a TV 
-show episode, given the full script."""
+SummScreenFD (Chen  et al., 2021) 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."""
 
 
 _QASPER_DESCRIPTION = """
-SummScreen (Boolean Questions, Clark et al., 2019a) is a summarization task where one should output the recap of a TV 
-show episode, given the full script."""
+Qasper (Dasigi et al., 2021) 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."""
 
 
 _QMSUM_DESCRIPTION = """
-SummScreen (Boolean Questions, Clark et al., 2019a) is a summarization task where one should output the recap of a TV 
-show episode, given the full script."""
-
+QMSum (Zhong et al., 2021) 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."""
 
 _NARRATIVE_QA_DESCRIPTION = """
-SummScreen (Boolean Questions, Clark et al., 2019a) is a summarization task where one should output the recap of a TV 
-show episode, given the full script."""
+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).."""
 
 
 _GOV_REPORT_DESCRIPTION = """
-SummScreen (Boolean Questions, Clark et al., 2019a) is a summarization task where one should output the recap of a TV 
-show episode, given the full script."""
+GovReport (Huang et al., 2021) 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."""
 
 
 _CONTRACT_NLI_DESCRIPTION = """
-SummScreen (Boolean Questions, Clark et al., 2019a) is a summarization task where one should output the recap of a TV 
-show episode, given the full script."""
+Contract NLI (Koreeda and Manning, 2021) 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."""
 
 
 _QUALITY_DESCRIPTION = """
-SummScreen (Boolean Questions, Clark et al., 2019a) is a summarization task where one should output the recap of a TV 
-show episode, given the full script."""
+QuALITY (Pang et al., 2021) 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."""
 
 
 _SUMM_SCREEN_CITATION = r"""
@@ -160,10 +180,10 @@ _QUALITY_CITATION = """\
 
 
 class ScrollsConfig(datasets.BuilderConfig):
-    """BuilderConfig for Scrolls."""
+    """BuilderConfig for SCROLLS."""
 
     def __init__(self, features, data_url, citation, url, **kwargs):
-        """BuilderConfig for Scrolls.
+        """BuilderConfig for SCROLLS.
         Args:
           features: `list[string]`, list of the features that will appear in the
             feature dict. Should not include "label".
@@ -175,13 +195,7 @@ class ScrollsConfig(datasets.BuilderConfig):
             'False' or 'True'.
           **kwargs: keyword arguments forwarded to super.
         """
-        # Version history:
-        # 1.0.2: Fixed non-nondeterminism in ReCoRD.
-        # 1.0.1: Change from the pre-release trial version of SuperGLUE (v1.9) to
-        #        the full release (v2.0).
-        # 1.0.0: S3 (new shuffling, sharding and slicing mechanism).
-        # 0.0.2: Initial version.
-        super(ScrollsConfig, self).__init__(version=datasets.Version("1.0.2"), **kwargs)
+        super(ScrollsConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
         self.features = features
         self.data_url = data_url
         self.citation = citation
@@ -195,7 +209,7 @@ class QualityConfig(ScrollsConfig):
 
 
 class Scrolls(datasets.GeneratorBasedBuilder):
-    """The SuperGLUE benchmark."""
+    """The SCROLLS benchmark."""
 
     features = ["id", "pid", "input", "output"]
     DEFAULT_WRITER_BATCH_SIZE = 1000  # because Narrative QA is a rather large dataset