TLDR9+ and TLDRHQ

This resource contains the resources introduced in TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts, including two Reddit-based TLDR summarization datasets. For more information about the construction stages of these datasets, please refer to the main paper.

Dataset links

You may download the introduced datasets from Google Drive links below:

Dataset	# instances	Link
TLDR9+	9,227,437	Download
TLDRHQ	1,671,099	Download

After downloading the datasets from links above, un-tar the compressed file by tar -xvf {DATASET_FILE}.tar. You will then find different data splits (datasets-m*/) in the root directory, each with 25001 instances.

Dataset structure

Each instance of TLDR9+ and TLDRHQ in the dataset has the following attributes:

• id: The ID of the reddit post,
• document: User's post text (source),
• summary: User-written summary/TL;DR of the post,
• ext_labels: Extractive labels of the post's sentences.
• rg_labels: The rouge scores of the post's sentences.

Notes:

• id is not the actual ID of the post in the Reddit discussion forum, but it's rather a generated ID that follows a specific format. For instance, *RS* (*RC*) format shows that the instance is of type Submission (Comment).
• document is split by the sentences; hence, you will find </s><s> tokens within the document's text, indicating the sentence boundaries.

Reddit Miner

We release the source implementation to collect the TL;DR instances (i.e., user_post and TL;DR summary pairs) from Pushshift data repository.

How to run

First, install the required package by running the following:

pip install -r requirements.txt

As the Pushshift files are quite large, we use the GNU Parallel shell tool for parallel processing. Hence, you will need install this shell tool.

For quick start, simply run: bash dl_uncompress.sh. The dl_uncompress.sh script calls the utilities to download, uncompress, and mine the instances to create the TL;DR data collection.

Some notes about the implementation are outlined below:

• fetch_file_links.py gathers the link of each compressed file (either comments/submissions that should be specified by the user) specified within a timeframe. It specifically takes in the following arguments:

  • -scrap: choose between comments or submissions. Default is set to comments.
  • -start_date: the starting date in the format of YYYY-MM (e.g. 2019-04).
  • -end_date: the ending date in the format of YYYY-MM (e.g. 2020-06).

• main.py is the main class that calls different utilities to filter the Reddit posts to those with TL;DRs. Arguments are listed below:

  • -mode: specifies the mode of the processing. It can be either "preprocess" or "comment_agg"
  • -read_dir: the input directory, where all uncompressed files Reddit files are located in.
  • -write_dir: the output directory to write the filtered instances one-by-one.
  • -tldr_th: word threshold for filtering TL;DRs. The instances that do not pass this threshold will be dropped.
  • -lower: a flag indicating either the crawled instances should be cased or uncased.

Citation

If you intend to use the data or code provided in this repo, please cite the following paper:

@inproceedings{Sotudeh2021Tldr9,
    title = "TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts",
    author = "Sotudeh, Sajad  and
      Deilamsalehy, Hanieh  and
      Dernoncourt, Franck  and
      Goharian, Nazli",
    booktitle = "Proceedings of the Third Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2021",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.newsum-1.15",
    pages = "142--151",
}

Contact

Please contact Sajad Sotudeh in case you have any question(s).