| _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""" | |
| }""" |