| | --- |
| | annotations_creators: |
| | - found |
| | language_creators: |
| | - found |
| | languages: |
| | - en |
| | licenses: |
| | - unknown |
| | multilinguality: |
| | - monolingual |
| | size_categories: |
| | - 10K<n<100K |
| | source_datasets: |
| | - original |
| | task_categories: |
| | - summarization |
| | task_ids: |
| | - summarization-other-paper-abstract-generation |
| | paperswithcode_id: multi-document |
| | pretty_name: Multi-Document |
| | --- |
| | # Dataset Card for Multi-Document |
| | ## Table of Contents |
| | - [Dataset Description](#dataset-description) |
| | - [Dataset Summary](#dataset-summary) |
| | - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
| | - [Languages](#languages) |
| | - [Dataset Structure](#dataset-structure) |
| | - [Data Instances](#data-instances) |
| | - [Data Fields](#data-fields) |
| | - [Data Splits](#data-splits) |
| | - [Dataset Creation](#dataset-creation) |
| | - [Curation Rationale](#curation-rationale) |
| | - [Source Data](#source-data) |
| | - [Annotations](#annotations) |
| | - [Personal and Sensitive Information](#personal-and-sensitive-information) |
| | - [Considerations for Using the Data](#considerations-for-using-the-data) |
| | - [Social Impact of Dataset](#social-impact-of-dataset) |
| | - [Discussion of Biases](#discussion-of-biases) |
| | - [Other Known Limitations](#other-known-limitations) |
| | - [Additional Information](#additional-information) |
| | - [Dataset Curators](#dataset-curators) |
| | - [Licensing Information](#licensing-information) |
| | - [Citation Information](#citation-information) |
| | - [Contributions](#contributions) |
| | ## Dataset Description |
| | - **Repository:** [Multi-Document repository](https://github.com/arka0821/multi_document_summarization) |
| | - **Paper:** [Multi-Document: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://arxiv.org/abs/2010.14235) |
| | ### Dataset Summary |
| | Multi-Document, a large-scale multi-document summarization dataset created from scientific articles. Multi-Document introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. |
| | ### Supported Tasks and Leaderboards |
| | [More Information Needed] |
| | ### Languages |
| | The text in the dataset is in English |
| | ## Dataset Structure |
| | ### Data Instances |
| | {"id": "n3ByHGrxH3bvfrvF", "docs": [{"id": "1394519630182457344", "text": "Clover Bio's COVID-19 vaccine candidate shows immune response against SARS-CoV-2 variants in mouse model https://t.co/wNWa9GQux5"}, {"id": "1398154482463170561", "text": "The purpose of the Vaccine is not to stop you from catching COVID 19. The vaccine introduces the immune system to an inactivated form of the SARS-CoV-2 coronavirus or a small part of it. This then equips the body with the ability to fight the virus better in case you get it. https://t.co/Cz9OU6Zi7P"}, {"id": "1354844652520792071", "text": "The Moderna mRNA COVID-19 vaccine appears to be effective against the novel, rapidly spreading variants of SARS-CoV-2.\nResearchers analysed blood samples from vaccinated people and monkeys- Both contained neutralising antibodies against the virus. \nPT1/2\n#COVID19vaccines #biotech https://t.co/ET1maJznot"}, {"id": "1340189698107518976", "text": "@KhandaniM Pfizer vaccine introduces viral surface protein which is constant accross SARS COV 2 variants into the body. Body builds antibodies against this protein, not any virus. These antibodies instructs macrophages & T-Cells to attack & destroy any COVID-19 v variant at infection point"}, {"id": "1374368989581778945", "text": "@DelthiaRicks \" Pfizer and BioNTech\u2019s COVID-19 vaccine is an mRNA vaccine, which does not use the live virus but rather a small portion of the viral sequence of the SARS-CoV-2 virus to instruct the body to produce the spike protein displayed on the surface of the virus.\""}, {"id": "1353354819315126273", "text": "Pfizer and BioNTech Publish Results of Study Showing COVID-19 Vaccine Elicits Antibodies that Neutralize Pseudovirus Bearing the SARS-CoV-2 U.K. Strain Spike Protein in Cell Culture | Pfizer https://t.co/YXcSnjLt8C"}, {"id": "1400821856362401792", "text": "Pfizer-BioNTech's covid-19 vaccine elicits lower levels of antibodies against the SARS-CoV-2\u00a0Delta variant\u00a0(B.1.617.2), first discovered in India, in comparison to other variants, said a research published in\u00a0Lancet\u00a0journal.\n https://t.co/IaCMX81X3b"}, {"id": "1367252963190665219", "text": "New research from UNC-Chapel Hill suggests that those who have previously experienced a SARS-CoV-2 infection develop a significant antibody response to the first dose of mRNA-based COVID-19 vaccine.\nhttps://t.co/B4vR1KUQ0w"}, {"id": "1375949502461394946", "text": "Mechanism of a COVID-19 nanoparticle vaccine candidate that elicits a broadly neutralizing antibody response to SARS-CoV-2 variants https://t.co/nc1L0uvtlI #bioRxiv"}, {"id": "1395428608349548550", "text": "JCI - Efficient maternal to neonatal transfer of antibodies against SARS-CoV-2 and BNT162b2 mRNA COVID-19 vaccine https://t.co/vIBcpPaKFZ"}], "summary": "The COVID-19 vaccine appears to be effective against the novel, rapidly spreading variants of SARS-CoV-2. Pfizer-BioNTech's COVID-19 vaccine use small portion of the viral sequence of the SARS-CoV-2 virus to equip the body with the ability to fight the virus better in case you get it."} |
| | ### Data Fields |
| | {'id': text of paper abstract \ |
| | 'docs': document id \ |
| | [ |
| | 'id': id of text \ |
| | 'text': text data \ |
| | ] |
| | 'summary': summary text |
| | } |
| | ### Data Splits |
| | The data is split into a training, validation and test. |
| | | train | validation | test | |
| | |------:|-----------:|-----:| |
| | | 50 | 10 | 5 | |
| | ## Dataset Creation |
| | ### Curation Rationale |
| | [More Information Needed] |
| | ### Source Data |
| | #### Initial Data Collection and Normalization |
| | [More Information Needed] |
| | #### Who are the source language producers? |
| | [More Information Needed] |
| | ### Annotations |
| | #### Annotation process |
| | [More Information Needed] |
| | #### Who are the annotators? |
| | [More Information Needed] |
| | ### Personal and Sensitive Information |
| | [More Information Needed] |
| | ## Considerations for Using the Data |
| | ### Social Impact of Dataset |
| | [More Information Needed] |
| | ### Discussion of Biases |
| | [More Information Needed] |
| | ### Other Known Limitations |
| | [More Information Needed] |
| | ## Additional Information |
| | ### Dataset Curators |
| | [More Information Needed] |
| | ### Licensing Information |
| | [More Information Needed] |
| | ### Citation Information |
| | ``` |
| | @article{lu2020multi, |
| | title={Multi-Document: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles}, |
| | author={Arka Das, India}, |
| | journal={arXiv preprint arXiv:2010.14235}, |
| | year={2022} |
| | } |
| | ``` |
| | ### Contributions |
| | Thanks to [@arka0821] (https://github.com/arka0821/multi_document_summarization) for adding this dataset. |
| |
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