{"yuvalkirstain--qmsum_t5_lm": {"description": "\nSCROLLS: Standardized CompaRison Over Long Language Sequences.\nA suite of natural language datasets that require reasoning over long texts.\nhttps://scrolls-benchmark.com/\n\nQMSum (Zhong et al., 2021) is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains. \nThe corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control, \nand committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues.\nAnnotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions, \nwhile ensuring that the relevant text for answering each query spans at least 200 words or 10 turns.", "citation": "@inproceedings{zhong-etal-2021-qmsum,\n    title = \"{QMS}um: A New Benchmark for Query-based Multi-domain Meeting Summarization\",\n    author = \"Zhong, Ming  and\n      Yin, Da  and\n      Yu, Tao  and\n      Zaidi, Ahmad  and\n      Mutuma, Mutethia  and\n      Jha, Rahul  and\n      Awadallah, Ahmed Hassan  and\n      Celikyilmaz, Asli  and\n      Liu, Yang  and\n      Qiu, Xipeng  and\n      Radev, Dragomir\",\n    booktitle = \"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n    month = jun,\n    year = \"2021\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.naacl-main.472\",\n    doi = \"10.18653/v1/2021.naacl-main.472\",\n    pages = \"5905--5921\",\n    abstract = \"Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics. In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and we introduce QMSum, a new benchmark for this task. QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple domains. Besides, we investigate a locate-then-summarize method and evaluate a set of strong summarization baselines on the task. Experimental results and manual analysis reveal that QMSum presents significant challenges in long meeting summarization for future research. Dataset is available at \\url{https://github.com/Yale-LILY/QMSum}.\",\n}\n\n@article{ TODO citation here\n}\nNote that each SCROLLS dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset.\n", "homepage": "https://github.com/Yale-LILY/QMSum", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "pid": {"dtype": "string", "id": null, "_type": "Value"}, "input": {"dtype": "string", "id": null, "_type": "Value"}, "output": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": null, "config_name": null, "version": null, "splits": {"train": {"name": "train", "num_bytes": 2934262, "num_examples": 1257, "dataset_name": "qmsum_t5_lm"}, "validation": {"name": "validation", "num_bytes": 633851, "num_examples": 272, "dataset_name": "qmsum_t5_lm"}, "test": {"name": "test", "num_bytes": 633851, "num_examples": 272, "dataset_name": "qmsum_t5_lm"}}, "download_checksums": null, "download_size": 953189, "post_processing_size": null, "dataset_size": 4201964, "size_in_bytes": 5155153}}