Add more information to README

README.md

@@ -11,8 +11,49 @@ configs:
     path: tables_dataset_viewer_only.jsonl
 ---
 
-# ArXivDIGESTables
+# ArxivDIGESTables
 
-**Note**: Please use huggingface datasets as a way to preview the data only as `load_dataset` does not work well with the current data format. Instead it is recommended to directly download `tables.jsonl`, `papers.jsonl`, and `full_texts.jsonl.gz`.
+The data set of tables released with [ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models](https://arxiv.org/abs/2410.22360).
 
-See [the github repo](https://github.com/bnewm0609/arxivDIGESTables) for more details.
+The tables in this dataset are arbitrary json objects, so they don't play nicely with huggingface's `load_dataset` method. The recommended way to access the data is to download individual files from [huggingface.co/datasets/blnewman/arxivDIGESTables](https://huggingface.co/datasets/blnewman/arxivDIGESTables/tree/main).
+
+For the high quality data, you should download `papers.jsonl`, `tables.jsonl`, and `full_texts.jsonl.gz`. 
+If you want more tables that are less stringently filtered and do not have associated full texts, you can download `papers_medium_quality.jsonl` and `tables_medium_quality.jsonl`.
+- The `papers*.jsonl` files include information about the papers cited in the tables including their S2 corpus ids, title, abstract and the ids of what tables they can be found in. These are mostly useful for analysis as almost all of the information is also included in the `tables*.jsonl` files.
+- The `tables*.jsonl` files include:
+    - `tabid`: an id for each table
+    - `table`: the table itself, which is a nested json dictionary
+    - `row_bib_map`: which maps each row of the table to the corpus id, title, and abstract for the paper cited in that row.
+    - `caption`: the table's caption
+    - `in_text_ref`: a list of paragraphs where the table is refered to in the main text
+    - `arxiv_id`: the arxiv id of the paper that table comes from
+- `full_text.jsonl.gz`contains the full texts for the papers in `papers.jsonl`.
+
+
+ If you want to preview the tables, you can use huggingface dataset's loader. In this case, the tables are stored as json strings and need to be parsed:
+ ```python
+ import json
+ from datasets import load_dataset
+
+ # high quality
+ tables = load_dataset("blnewman/arxivDIGESTables")
+
+# load the table from json string. Not necessary if you download `tables.jsonl` directly.
+print(json.loads(tables["validation"]["table"][0]))
+
+# medium quality
+ arxivdigestables_medium = load_dataset("blnewman/arxivDIGESTables", "medium_quality")
+ ```
+
+See [the github repo](https://github.com/bnewm0609/arxivDIGESTables) for more details.
+
+## Citation
+```
+@article{newman2024arxivdigestables,
+      title={ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models}, 
+      author={Benjamin Newman and Yoonjoo Lee and Aakanksha Naik and Pao Siangliulue and Raymond Fok and Juho Kim and Daniel S. Weld and Joseph Chee Chang and Kyle Lo},
+      year={2024},
+      journal={arXiv preprint},
+      url={https://arxiv.org/abs/2410.22360}, 
+}
+```