Datasets:
File size: 6,257 Bytes
543230b d91e768 543230b d91e768 731b87e d91e768 543230b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 |
---
task_categories:
- summarization
language:
- en
tags:
- chemistry
- biology
- medical
pretty_name: Generating Abstracts of Academic Chemistry Papers
size_categories:
- 100K<n<1M
---
# Dataset Card for ChemSum
## ChemSum Description
<!---- **Homepage:**
- **Leaderboard:**
----->
- **Paper:** [What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization ](https://arxiv.org/abs/2305.07615)
- **Journal:** ACL 2023
- **Point of Contact:** [email protected]
- **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](https://www.selenium.dev/documentation/webdriver/).
Each PDF was processed with Grobid via a locally installed [client](https://pypi.org/project/grobid-client-python/) 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).
| Source | # of Articles |
| ----------- | ----------- |
| Beilstein | 1,829 |
| Chem Cell | 546 |
| ChemRxiv | 12,231 |
| Chemistry Open | 398 |
| Nature Communications Chemistry | 572 |
| PubMed Author Manuscript | 57,680 |
| PubMed Open Access | 29,540 |
| Royal Society of Chemistry (RSC) | 9,334 |
| Scientific Reports - Nature | 6,826 |
<!---
### Supported Tasks and Leaderboards
[More Information Needed]
--->
### Languages
English
## Dataset Structure
<!--- ### Data Instances --->
### Data Fields
| Column | Description |
| ----------- | ----------- |
| `uuid` | Unique Identifier for the Example |
| `title` | Title of the Article |
| `article_source` | Open Source Journal (see above for list) |
| `abstract` | Abstract (summary reference) |
| `sections` | Full-text sections from the main body of paper (<!> indicates section boundaries)|
| `headers` | Corresponding section headers for `sections` field (<!> delimited) |
| `source_toks` | Aggregate number of tokens across `sections` |
| `target_toks` | Number of tokens in the `abstract` |
| `compression` | Ratio of `source_toks` to `target_toks` |
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
| Split | Count |
| ----------- | ----------- |
| `train` | 115,956 |
| `validation` | 1,000 |
| `test` | 2,000 |
### Citation Information
```
@inproceedings{adams-etal-2023-desired,
title = "What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization",
author = "Adams, Griffin and
Nguyen, Bichlien 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",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.587",
doi = "10.18653/v1/2023.acl-long.587",
pages = "10520--10542",
abstract = "Summarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE). To address this, recent work has added a calibration step, which exposes a model to its own ranked outputs to improve relevance or, in a separate line of work, contrasts positive and negative sets to improve faithfulness. While effective, much of this work has focused on \textit{how} to generate and optimize these sets. Less is known about \textit{why} one setup is more effective than another. In this work, we uncover the underlying characteristics of effective sets. For each training instance, we form a large, diverse pool of candidates and systematically vary the subsets used for calibration fine-tuning. Each selection strategy targets distinct aspects of the sets, such as lexical diversity or the size of the gap between positive and negatives. On three diverse scientific long-form summarization datasets (spanning biomedical, clinical, and chemical domains), we find, among others, that faithfulness calibration is optimal when the negative sets are extractive and more likely to be generated, whereas for relevance calibration, the metric margin between candidates should be maximized and surprise{--}the disagreement between model and metric defined candidate rankings{--}minimized.",
}
```
<!---
## 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]
### Contributions
[More Information Needed]
--->
|