Datasets:
Tasks:
Summarization
Languages:
English
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
ArXiv:
License:
| """ | |
| HF dataset loading script | |
| """ | |
| import re | |
| from pathlib import Path | |
| import datasets | |
| import pandas as pd | |
| _DESCRIPTION = """Update-background tuples for 14 news event timelines.""" | |
| _URLS = { | |
| "events": "events.tar.gz", | |
| "train": "splits/train.txt", | |
| "dev": "splits/dev.txt", | |
| "test": "splits/test.txt", | |
| } | |
| _CITATION = """\ | |
| @article{pratapa-etal-2023-background, | |
| title = {Background Summarization of Event Timelines}, | |
| author = {Pratapa, Adithya and Small, Kevin and Dreyer, Markus}, | |
| publisher = {EMNLP}, | |
| year = {2023} | |
| } | |
| """ | |
| _HOMEPAGE = "https://github.com/amazon-science/background-summaries" | |
| _LICENSE = "CC-BY-NC-4.0" | |
| class BackgroundSummConfig(datasets.BuilderConfig): | |
| def __init__(self, features, **kwargs) -> None: | |
| super().__init__(version=datasets.Version("1.0.0"), **kwargs) | |
| self.features = features | |
| class BackgroundSumm(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| BUILDER_CONFIGS = [ | |
| BackgroundSummConfig( | |
| name="background-summ", | |
| description=_DESCRIPTION, | |
| features=["src", "tgt", "z"], | |
| ) | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| {field: datasets.Value("string") for field in ["src", "tgt", "z"]} | |
| ), | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| dl_files = dl_manager.download_and_extract(_URLS) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "events_path": Path(dl_files["events"]), | |
| "splits_path": Path(dl_files["train"]), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "events_path": Path(dl_files["events"]), | |
| "splits_path": Path(dl_files["dev"]), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "events_path": Path(dl_files["events"]), | |
| "splits_path": Path(dl_files["test"]), | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, events_path: Path, splits_path: Path): | |
| # load events for the split | |
| with open(splits_path, "r") as rf: | |
| event_names = [line.strip() for line in rf.readlines()] | |
| data_idx = 0 | |
| for event in event_names: | |
| # separately load update and background summaries for each annotator | |
| annotators = ["annotator1", "annotator2", "annotator3"] | |
| for ann in annotators: | |
| # load tsv path | |
| tsv_path = events_path / "events" / event / f"{ann}.tsv" | |
| df = pd.read_csv(tsv_path, sep="\t") | |
| df = df.fillna("") | |
| timestamps, updates, backgrounds = [], [], [] | |
| for idx, row in enumerate(df.itertuples()): | |
| ts = row.Date.strip("[]") | |
| update = row.Update.replace("\\n", " ") | |
| update = re.sub(r"[ ]+", r" ", update).strip() | |
| background = row.Background.replace("\\n", " ") | |
| background = re.sub(r"[ ]+", r" ", background).strip() | |
| timestamps += [ts] | |
| updates += [update] | |
| backgrounds += [background] | |
| # source is a timestamped concatenation of past updates | |
| src = [ | |
| f"Date: {_ts}, Update: {_update}" | |
| for _ts, _update in zip(timestamps[:-1], updates[:-1]) | |
| ] | |
| src = " ".join(src) | |
| # target is current background | |
| tgt = backgrounds[-1] | |
| # guidance is current update | |
| z = f"Date: {ts}, Update: {updates[-1]}" | |
| if idx > 0: | |
| yield data_idx, {"src": src, "tgt": tgt, "z": z} | |
| data_idx += 1 | |