Datasets:
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"""
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
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