README.md

metadata

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). This would be convenient if you want to train transformer models using our dataset.

Just do,

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