Update README.md

README.md

@@ -1,20 +1,70 @@
 ---
-task_categories:
-- text-classification
 language:
 - en
 tags:
 - legal
 - finance
 - climate
+- social
 pretty_name: 'CoCoHD: Congress Committee Hearing Dataset - Transcripts'
 size_categories:
 - 10K<n<100K
+license: cc
 ---
 
-# CoCoHD: Congress Committee Hearing Dataset - Transcripts
-Related datasets: 
- - https://huggingface.co/datasets/gtfintechlab/CoCoHD_hearing_details
- - https://huggingface.co/datasets/gtfintechlab/CoCoHD_hearing_details_cleaned
+## Dataset Summary
 
-This dataset contains 30k+ U.S. Congressional hearing transcripts from the 105th Congress to the 118th. The hearing transcripts of each Congress session are kept in a folder. The transcripts themselves are in txt format. 
+The Congress Committee Hearing Dataset (CoCoHD) Transcripts comprises transcripts from congressional (House and Senate) hearings from 1997 to 2024. 
+
+This dataset is designed to facilitate research in natural language processing (NLP). It contains 30k+ U.S. congressional hearing transcripts from the 105th Congress to the 118th. 
+
+## Dataset Structure
+
+The hearing transcripts of each Congress session are kept in a folder and in text format. 
+
+### Related Datasets
+
+- [CoCoHD Hearing Details](https://huggingface.co/datasets/gtfintechlab/CoCoHD_hearing_details): This dataset provides comprehensive metadata for each congressional hearing, including information such as the hearing title, date, committee, and witnesses.
+
+- [CoCoHD Hearing Details Cleaned](https://huggingface.co/datasets/gtfintechlab/CoCoHD_hearing_details_cleaned): A refined version of the hearing details dataset, this collection has been processed to correct inconsistencies, standardize committee names, and remove duplicate or erroneous records, ensuring higher data quality for analysis.
+
+These datasets offer valuable metadata that complements the CoCoHD transcripts, enabling more detailed and accurate analyses of congressional hearings. 
+
+## Licensing
+
+The CoCoHD Transcripts dataset is released under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/). This permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
+
+## Citation
+
+If you utilize this dataset in your research, please cite it as follows:
+
+```
+@inproceedings{hiray-etal-2024-cocohd,
+    title = "{C}o{C}o{HD}: Congress Committee Hearing Dataset",
+    author = "Hiray, Arnav  and
+      Liu, Yunsong  and
+      Song, Mingxiao  and
+      Shah, Agam  and
+      Chava, Sudheer",
+    editor = "Al-Onaizan, Yaser  and
+      Bansal, Mohit  and
+      Chen, Yun-Nung",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
+    month = nov,
+    year = "2024",
+    address = "Miami, Florida, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.findings-emnlp.911",
+    doi = "10.18653/v1/2024.findings-emnlp.911",
+    pages = "15529--15542",
+    abstract = "U.S. congressional hearings significantly influence the national economy and social fabric, impacting individual lives. Despite their importance, there is a lack of comprehensive datasets for analyzing these discourses. To address this, we propose the **Co**ngress **Co**mmittee **H**earing **D**ataset (CoCoHD), covering hearings from 1997 to 2024 across 86 committees, with 32,697 records. This dataset enables researchers to study policy language on critical issues like healthcare, LGBTQ+ rights, and climate justice. We demonstrate its potential with a case study on 1,000 energy-related sentences, analyzing the Energy and Commerce Committee{'}s stance on fossil fuel consumption. By fine-tuning pre-trained language models, we create energy-relevant measures for each hearing. Our market analysis shows that natural language analysis using CoCoHD can predict and highlight trends in the energy sector.",
+}
+```
+
+## GitHub Link
+ - [Link to our GitHub repository.](https://github.com/gtfintechlab/CoCoHD)
+
+
+## Contact Information
+
+Please contact Agam Shah (ashah482[at]gatech[dot]edu) for any issues and questions.