move citations and descriptions to another module

citations_and_descriptions.py

@@ -0,0 +1,56 @@
+_FS_CITATION = """
+TBD
+"""
+
+_FS_DESCRIPTION = """
+TBD
+"""
+
+_SUMM_SCREEN_DESCRIPTION = """
+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."""
+
+_GOV_REPORT_DESCRIPTION = """
+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."""
+
+_ARXIV_DESCRIPTION = """
+"""
+
+_SUMM_SCREEN_CITATION = r"""
+@misc{chen2021summscreen,
+      title={SummScreen: A Dataset for Abstractive Screenplay Summarization}, 
+      author={Mingda Chen and Zewei Chu and Sam Wiseman and Kevin Gimpel},
+      year={2021},
+      eprint={2104.07091},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}"""
+
+_GOV_REPORT_CITATION = r"""
+@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.",
+}"""
+
+_ARXIV_CITATION = r"""
+}"""

fs.py

@@ -4,70 +4,13 @@
 
 import json
 import os
-from abc import abstractmethod
-
 import datasets
-from datasets import load_dataset
-from transformers import AutoTokenizer  # TODO comment out when getting rid of __main__:
-
-_FS_CITATION = """
-TBD
-"""
-
-_FS_DESCRIPTION = """
-TBD
-"""
-
-_SUMM_SCREEN_DESCRIPTION = """
-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."""
-
-_GOV_REPORT_DESCRIPTION = """
-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."""
-
-_ARXIV_DESCRIPTION = """
-"""
-
-_SUMM_SCREEN_CITATION = r"""
-@misc{chen2021summscreen,
-      title={SummScreen: A Dataset for Abstractive Screenplay Summarization}, 
-      author={Mingda Chen and Zewei Chu and Sam Wiseman and Kevin Gimpel},
-      year={2021},
-      eprint={2104.07091},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}"""
-
-_GOV_REPORT_CITATION = r"""
-@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.",
-}"""
-
-_ARXIV_CITATION = r"""
-}"""
-
-SUMM_PROMPT = "Summary: "
+from citations_and_descriptions import (
+    _SUMM_SCREEN_DESCRIPTION, _SUMM_SCREEN_CITATION,
+    _GOV_REPORT_CITATION, _GOV_REPORT_DESCRIPTION,
+    _ARXIV_CITATION, _ARXIV_DESCRIPTION,
+    _FS_DESCRIPTION, _FS_CITATION
+)
 
 
 class FSConfig(datasets.BuilderConfig):
@@ -116,8 +59,6 @@ class FSConfig(datasets.BuilderConfig):
 class ScrollsConfig(FSConfig):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
-        self.prompt = SUMM_PROMPT
-
         self.train_file = "train.jsonl"
         self.validation_file = "validation.jsonl"
         self.test_file = "test.jsonl"
@@ -137,8 +78,6 @@ class ScrollsConfig(FSConfig):
 class ArxivConfig(FSConfig):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
-        self.prompt = SUMM_PROMPT
-
         self.train_file = "train.txt"
         self.validation_file = "val.txt"
         self.test_file = "test.txt"