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---
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")
```
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