---
license: mit
task_categories:
- summarization
language:
- en
pretty_name: aclsum
size_categories:
- n<1K
configs:
- config_name: abstractive
  default: true
  data_files:
  - split: train
    path: "abstractive/train.jsonl"
  - split: validation
    path: "abstractive/val.jsonl"
  - split: test
    path: "abstractive/test.jsonl"
- config_name: extractive
  data_files:
  - split: train
    path: "extractive/train.jsonl"
  - split: validation
    path: "extractive/val.jsonl"
  - split: test
    path: "extractive/test.jsonl"
---

# ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications

This repository contains data for our paper "ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications" and a small 
utility class to work with it.


## HuggingFace datasets

You can also use Huggin Face datasets to load ACLSum ([dataset link](https://huggingface.co/datasets/sobamchan/aclsum)).
This would be convenient if you want to train transformer models using our dataset.

Just do,

```py
from datasets import load_dataset
dataset = load_dataset("sobamchan/aclsum", "challenge", split="train")
```