Datasets:

armanc
/

scientific_papers

- {"arxiv": {"description": "\nScientific papers datasets contains two sets of long and structured documents.\nThe datasets are obtained from ArXiv and PubMed OpenAccess repositories.\n\nBoth \"arxiv\" and \"pubmed\" have two features:\n - article: the body of the document, pagragraphs seperated by \"/n\".\n - abstract: the abstract of the document, pagragraphs seperated by \"/n\".\n - section_names: titles of sections, seperated by \"/n\".\n\n", "citation": "\n@article{Cohan_2018,\n title={A Discourse-Aware Attention Model for Abstractive Summarization of\n Long Documents},\n url={http://dx.doi.org/10.18653/v1/n18-2097},\n DOI={10.18653/v1/n18-2097},\n journal={Proceedings of the 2018 Conference of the North American Chapter of\n the Association for Computational Linguistics: Human Language\n Technologies, Volume 2 (Short Papers)},\n publisher={Association for Computational Linguistics},\n author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},\n year={2018}\n}\n", "homepage": "https://github.com/armancohan/long-summarization", "license": "", "features": {"article": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "section_names": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "scientific_papers", "config_name": "arxiv", "version": {"version_str": "1.1.1", "description": null, "major": 1, "minor": 1, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 7148341992, "num_examples": 203037, "dataset_name": "scientific_papers"}, "validation": {"name": "validation", "num_bytes": 217125524, "num_examples": 6436, "dataset_name": "scientific_papers"}, "test": {"name": "test", "num_bytes": 217514961, "num_examples": 6440, "dataset_name": "scientific_papers"}}, "download_checksums": {"https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip": {"num_bytes": 3624420843, "checksum": "82ed30dd7c66a6497eeb3d7c3090c274e9e32c012438f8e0bb3cce3e6c1fcada"}, "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip": {"num_bytes": 880225504, "checksum": "d424074726a5e29e20bf834055fe7efe90f8a37bce0a2b512e4ab7e487013c04"}}, "download_size": 4504646347, "post_processing_size": null, "dataset_size": 7582982477, "size_in_bytes": 12087628824}, "pubmed": {"description": "\nScientific papers datasets contains two sets of long and structured documents.\nThe datasets are obtained from ArXiv and PubMed OpenAccess repositories.\n\nBoth \"arxiv\" and \"pubmed\" have two features:\n - article: the body of the document, pagragraphs seperated by \"/n\".\n - abstract: the abstract of the document, pagragraphs seperated by \"/n\".\n - section_names: titles of sections, seperated by \"/n\".\n\n", "citation": "\n@article{Cohan_2018,\n title={A Discourse-Aware Attention Model for Abstractive Summarization of\n Long Documents},\n url={http://dx.doi.org/10.18653/v1/n18-2097},\n DOI={10.18653/v1/n18-2097},\n journal={Proceedings of the 2018 Conference of the North American Chapter of\n the Association for Computational Linguistics: Human Language\n Technologies, Volume 2 (Short Papers)},\n publisher={Association for Computational Linguistics},\n author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},\n year={2018}\n}\n", "homepage": "https://github.com/armancohan/long-summarization", "license": "", "features": {"article": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "section_names": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "scientific_papers", "config_name": "pubmed", "version": {"version_str": "1.1.1", "description": null, "major": 1, "minor": 1, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 2252027383, "num_examples": 119924, "dataset_name": "scientific_papers"}, "validation": {"name": "validation", "num_bytes": 127403398, "num_examples": 6633, "dataset_name": "scientific_papers"}, "test": {"name": "test", "num_bytes": 127184448, "num_examples": 6658, "dataset_name": "scientific_papers"}}, "download_checksums": {"https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/arxiv-dataset.zip": {"num_bytes": 3624420843, "checksum": "82ed30dd7c66a6497eeb3d7c3090c274e9e32c012438f8e0bb3cce3e6c1fcada"}, "https://s3.amazonaws.com/datasets.huggingface.co/scientific_papers/1.1.1/pubmed-dataset.zip": {"num_bytes": 880225504, "checksum": "d424074726a5e29e20bf834055fe7efe90f8a37bce0a2b512e4ab7e487013c04"}}, "download_size": 4504646347, "post_processing_size": null, "dataset_size": 2506615229, "size_in_bytes": 7011261576}}