|
|
| --- |
| language: |
| - en |
| bigbio_language: |
| - English |
| license: mit |
| multilinguality: monolingual |
| bigbio_license_shortname: MIT |
| pretty_name: Multi-XScience |
| homepage: https://github.com/yaolu/Multi-XScience |
| bigbio_pubmed: False |
| bigbio_public: True |
| bigbio_tasks: |
| - PARAPHRASING |
| - SUMMARIZATION |
| --- |
| |
|
|
| # Dataset Card for Multi-XScience |
|
|
| ## Dataset Description |
|
|
| - **Homepage:** https://github.com/yaolu/Multi-XScience |
| - **Pubmed:** False |
| - **Public:** True |
| - **Tasks:** PARA,SUM |
|
|
|
|
| Multi-document summarization is a challenging task for which there exists little large-scale datasets. |
| We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. |
| Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section |
| of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, |
| a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and |
| empirical results---using several state-of-the-art models trained on the Multi-XScience dataset---reveal t |
| hat Multi-XScience is well suited for abstractive models. |
|
|
|
|
|
|
| ## Citation Information |
|
|
| ``` |
| @misc{https://doi.org/10.48550/arxiv.2010.14235, |
| doi = {10.48550/ARXIV.2010.14235}, |
| |
| url = {https://arxiv.org/abs/2010.14235}, |
| |
| author = {Lu, Yao and Dong, Yue and Charlin, Laurent}, |
| |
| keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
| |
| title = {Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles}, |
| |
| publisher = {arXiv}, |
| |
| year = {2020}, |
| |
| copyright = {arXiv.org perpetual, non-exclusive license} |
| } |
| |
| ``` |
|
|
