Dataset Card for ChemSum

ChemSum Description

• Paper: What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization
• Journal: ACL 2023
• Point of Contact: griffin.adams@columbia.edu
• Repository: https://github.com/griff4692/calibrating-summaries

ChemSum Summary

We introduce a dataset with a pure chemistry focus by compiling a list of chemistry academic journals with Open-Access articles. For each journal, we downloaded full-text article PDFs from the Open-Access portion of the journal using available APIs, or scraping this content using Selenium Chrome WebDriver . Each PDF was processed with Grobid via a locally installed client to extract free-text paragraphs with sections.

The table below shows the journals from which Open Access articles were sourced, as well as the number of papers processed.

For all journals, we filtered for papers with the provided topic of Chemistry when papers from other disciplines were also available (e.g. PubMed).

Languages

English

Dataset Structure

Data Fields

Please refer to load_chemistry() in https://github.com/griff4692/calibrating-summaries/blob/master/preprocess/preprocess.py for pre-processing as a summarization dataset. The inputs are sections and headers and the targets is the abstract .

Data Splits

Citation Information

@article{adams2023desired,
  title={What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization},
  author={Adams, Griffin and Nguyen, Bichlien H and Smith, Jake and Xia, Yingce and Xie, Shufang and Ostropolets, Anna and Deb, Budhaditya and Chen, Yuan-Jyue and Naumann, Tristan and Elhadad, No{\'e}mie},
  journal={arXiv preprint arXiv:2305.07615},
  year={2023}
}