adithya7/background-summaries

Dataset Card for Background Summarization of Event Timelines

This dataset provides background text summaries for news events timelines.

Dataset Details

Dataset Description

Generating concise summaries of news events is a challenging natural language processing task. While journalists often curate timelines to highlight key sub-events, newcomers to a news event face challenges in catching up on its historical context. This dataset addresses this need by introducing the task of background news summarization, which complements each timeline update with a background summary of relevant preceding events. This dataset includes human-annotated backgrounds for 14 major news events from 2005--2014.

• Curated by: Adithya Pratapa, Kevin Small, Markus Dreyer
• Language(s) (NLP): English
• License: CC-BY-NC-4.0

Dataset Sources

• Repository: https://github.com/amazon-science/background-summaries
• Paper: https://arxiv.org/abs/2310.16197

Uses

Direct Use

This dataset can be used for training text summarization systems. The trained systems would be capable of generating background (historical context) to a news update. To generate the background, the system takes past news updates as input.

Out-of-Scope Use

Systems trained on this dataset might not perform as expected on domains other than newswire. To avoid factual errors, system-generated summaries should be verified by experts before deploying in real-world.

Dataset Structure

Dataset Fields

Field	Name	Description
src	Source	Concatenated string of all the previous updates. Each update text includes the publication date.
z	Guidance	Update text for the current timestep.
tgt	Target	Background text for the current timestep.

Data Splits

An overview of the major events and their splits in this dataset. The last column provides the statistics for background annotations provided in this dataset.

Split	Major event	Sources (# timelines)	Time period	# updates	len(updates)	len(background)
Train	Swine flu	T17 (3)	2009	21	52	45
Train	Financial crisis	T17 (1)	2008	65	115	147
Train	Iraq war	T17 (1)	2005	155	41	162
Validation	Haitian earthquake	T17 (1)	2010	11	100	61
Validation	Michael Jackson death	T17 (1)	2009--2011	37	36	164
Validation	BP oil spill	T17 (5)	2010--2012	118	56	219
Test	NSA leak	SocialTimeline (1)	2014	29	45	50
Test	Gaza conflict	SocialTimeline (1)	2014	38	183	263
Test	MH370 flight disappearance	SocialTimeline (1)	2014	39	39	127
Test	Yemen crisis	Crisis (6)	2011--2012	81	30	125
Test	Russian-Ukraine conflict	SocialTimeline (3)	2014	86	112	236
Test	Libyan crisis	T17 (2); Crisis (7)	2011	118	38	177
Test	Egyptian crisis	T17 (1); Crisis (4)	2011--2013	129	34	187
Test	Syrian crisis	T17 (4); Crisis (5)	2011--2013	164	30	162

Dataset Creation

Curation Rationale

Readers often find it difficult to keep track of complex news events. A background summary that provides sufficient historical context can help improve the reader's understanding of a news update. This dataset provides human-annotated backgrounds for development and evaluation of background summarization systems.

Source Data

Data Collection and Processing

This dataset is built upon three popular news timeline summarization datasets, Timeline17 (Binh Tran et al., 2013), Crisis (Tran et al., 2015), and Social Timeline (Wang et al., 2015).

Who are the source data producers?

Timeline17: compiled from an ensemble of news websites, this dataset provides 17 timelines spanning 9 major events from 2005--2013.

Crisis: a follow-up to the Timeline17 dataset, this covers 25 timelines spanning 4 major events. While it mostly covers a subset of events from Timeline17, it adds a new event (the Yemen crisis).

Social Timeline: compiled 6 timelines covering 4 major events from 2014. The timelines were collected from Wikipedia, NYTimes, and BBC.

Annotations

Annotation process

Timelines were originally collected from various news websites (CNN, BBC, NYTimes, etc.), many events have more than one timeline. Since each timeline covers the same underlying event, we merge them using timestamps to create a single timeline per event. During this merging process, we often end up with more than one update text per timestamp with possibly duplicate content. We ask the annotators to first rewrite the input updates to remove any duplicate content. Our annotation process for each news event contains the following three steps:

1. Read the input timeline to get a high-level understanding of the event.
2. For each timestep, read the provided 'rough' update summary. Rewrite the update into a short paragraph, removing any duplicate or previously reported subevents.
3. Go through the timeline in a sequential manner and write a background summary for each timestep.

Who are the annotators?

We hired three professional annotators. For each timeline, we collect three independent (rewritten) update and (new) background pairs.

Personal and Sensitive Information

To the best of our knowledge, there is no personal or sensitive information in this dataset.

Bias, Risks, and Limitations

Limitations

Personalized Backgrounds: While a background summary can be useful to any news reader, the utility can vary depending on the reader's familiarity with the event. This dataset doesn't include any backgrounds customized to individual readers.

Local Events: This dataset is limited to globally popular events involving disasters and conflicts. We leave the task of collecting background summaries for local events to future work.

Background from News Articles: Background summaries can also be generated directly from news articles. In this dataset, we only consider background summaries based on past news updates. We leave the extension to news articles to future work.

Citation

BibTeX:

@article{pratapa-etal-2023-background,
title = {Background Summarization of Event Timelines},
author = {Pratapa, Adithya and Small, Kevin and Dreyer, Markus},
publisher = {EMNLP},
year = {2023},
url = {https://arxiv.org/abs/2310.16197},
}

Glossary

Major event: the key news story for which we are constructing a timeline. For instance, 'Egyptian Crisis', 'BP oil spill', 'MH 370 disappearance' are some of the super events from our dataset.

Timeline: a series of timesteps. Each timestep in a timeline is associated with an update and a background summary.

Timestep: day of the event (yyyy-mm-dd).

Update: a short text summary of what's new in the news story. This text summarizes the latest events, specifically ones that are important to the overall story.

Background: a short text summary that provides sufficient historical context for the current update. Background aims to provide the reader a quick history of the news story, without them having to read all the previous updates. Background should cover past events that help in understanding the current events described in the update.

Dataset Card Authors

Adithya Pratapa, Kevin Small, Markus Dreyer

Dataset Card Contact

Adithya Pratapa