| 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") | |
| ``` | |