Datasets:

roemmele
/

ablit

original stringlengths 55 65.2k	abridged stringlengths 53 43.2k	book stringclasses 10 values	chapter stringlengths 9 121
Now do those two gentlemen not very neat about the cuffs and buttons who attended the last coroner's inquest at the Sol's Arms reappear in the precincts with surprising swiftness (being, in fact, breathlessly fetched by the active and intelligent beadle), and institute perquisitions through the court, and dive into the...	Those two gentlemen who attended the last coroner's inquest at the Sol's Arms reappear in the area with surprising swiftness, make enquiries through the court, and write notes. They note down how the neighbourhood of Chancery Lane was yesterday, at about midnight, thrown into a state of the most intense excitement by a...	Bleak House	Chapter 33: Interlopers
It is night in Lincoln's Inn--perplexed and troublous valley of the shadow of the law, where suitors generally find but little day--and fat candles are snuffed out in offices, and clerks have rattled down the crazy wooden stairs and dispersed. The bell that rings at nine o'clock has ceased its doleful clangour about no...	It is night in Lincoln's Inn - perplexed and troublous valley of the shadow of the law - and fat candles are snuffed out in offices, and clerks have dispersed. The gates are shut; and the night-porter keeps guard in his lodge. From tiers of staircase windows clogged lamps dimly blink at the stars. In dirty upper caseme...	Bleak House	Chapter 32: The Appointed Time
Wintry morning, looking with dull eyes and sallow face upon the neighbourhood of Leicester Square, finds its inhabitants unwilling to get out of bed. Many of them are not early risers at the brightest of times, being birds of night who roost when the sun is high and are wide awake and keen for prey when the stars shine...	Wintry morning, looking with dull eyes upon the neighbourhood of Leicester Square, finds its inhabitants unwilling to get out of bed. Many of them are not early risers at the brightest of times, being birds of night. Behind dingy blind and curtain, skulking under false names, false hair, false jewellery, and false hist...	Bleak House	Chapter 26: Sharpshooters
It was three o'clock in the morning when the houses outside London did at last begin to exclude the country and to close us in with streets. We had made our way along roads in a far worse condition than when we had traversed them by daylight, both the fall and the thaw having lasted ever since; but the energy of my com...	It was three o'clock in the morning when London at last began to close us in with streets. We had made our way along roads in a far worse condition than on the previous day; but my companion's energy never slackened. Whenever the horses had stopped exhausted half-way up hills, or had slipped and become entangled with t...	Bleak House	Chapter 59: Esther's Narrative
"When Mr. Woodcourt arrived in London, he went, that very same day, to Mr. Vholes's in Symond's Inn.(...TRUNCATED)	"When Mr. Woodcourt arrived in London, he went the same day to Mr. Vholes's in Symond's Inn. For aft(...TRUNCATED)	Bleak House	Chapter 51: Enlightened
"There is a hush upon Chesney Wold in these altered days, as there is upon a portion of the family h(...TRUNCATED)	"There is a hush upon Chesney Wold in these altered days. The handsome Lady Dedlock lies in the maus(...TRUNCATED)	Bleak House	Chapter 66: Down in Lincolnshire
"The long vacation saunters on towards term-time like an idle river very leisurely strolling down a (...TRUNCATED)	"The long vacation saunters on like an idle river leisurely strolling down a flat country to the sea(...TRUNCATED)	Bleak House	Chapter 20: A New Lodger
"I had not been at home again many days when one evening I went upstairs into my own room to take a (...TRUNCATED)	"I had not been at home many days when one evening I went upstairs to my own room to see how Charley(...TRUNCATED)	Bleak House	Chapter 31: Nurse and Patient
"We came home from Mr. Boythorn's after six pleasant weeks. We were often in the park and in the woo(...TRUNCATED)	"We came home from Mr. Boythorn's after six pleasant weeks. We were often in the park and woods, and(...TRUNCATED)	Bleak House	Chapter 23: Esther's Narrative
"When our time came for returning to Bleak House again, we were punctual to the day and were receive(...TRUNCATED)	"When we returned to Bleak House, we were received with an overpowering welcome. I was perfectly res(...TRUNCATED)	Bleak House	Chapter 38: A Struggle

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Dataset Card for AbLit

Dataset Summary

The AbLit dataset contains abridged versions of 10 classic English literature books, aligned with their original versions on various passage levels. The abridgements were written and made publically available by Emma Laybourn here. This is the first known dataset for NLP research that focuses on the abridgement task.

See the paper for a detailed description of the dataset, as well as the results of several modeling experiments. The GitHub repo also provides more extensive ways to interact with the data beyond what is provided here.

Languages

English

Dataset Structure

Each passage in the original version of a book chapter is aligned with its corresponding passage in the abridged version. These aligned pairs are available for various passage sizes: sentences, paragraphs, and multi-paragraph "chunks". The passage size is specified when loading the dataset. There are train/dev/test splits for items of each size.

Passage Size	Description	# Train	# Dev	# Test
chapters	Each passage is a single chapter	808	10	50
sentences	Each passage is a sentence delimited by the NLTK sentence tokenizer	122,219	1,143	10,431
paragraphs	Each passage is a paragraph delimited by a line break	37,227	313	3,125
chunks-10-sentences	Each passage consists of up to X=10 number of sentences, which may span more than one paragraph. To derive chunks with other lengths X, see GitHub repo above	14,857	141	1,264

Example Usage

To load aligned paragraphs:

from datasets import load_dataset
data = load_dataset("roemmele/ablit", "paragraphs")

Data Fields

original: passage text in the original version
abridged: passage text in the abridged version
book: title of book containing passage
chapter: title of chapter containing passage

Dataset Creation

Curation Rationale

Abridgement is the task of making a text easier to understand while preserving its linguistic qualities. Abridgements are different from typical summaries: whereas summaries abstractively describe the original text, abridgements simplify the original primarily through a process of extraction. We present this dataset to promote further research on modeling the abridgement process.

Source Data

The author Emma Laybourn wrote abridged versions of classic English literature books available through Project Gutenberg. She has also provided her abridgements for free on her website. This is how she describes her work: “This is a collection of famous novels which have been shortened and slightly simplified for the general reader. These are not summaries; each is half to two-thirds of the original length. I’ve selected works that people often find daunting because of their density or complexity: the aim is to make them easier to read, while keeping the style intact.”

Initial Data Collection and Normalization

We obtained the original and abridged versions of the books from the respective websites.

Who are the source language producers?

Emma Laybourn

Annotations

Annotation process

We designed a procedure for automatically aligning passages between the original and abridged version of each chapter. We conducted a human evaluation to verify these alignments had high accuracy. The training split of the dataset has ~99% accuracy. The dev and test splits of the dataset were fully human-validated to ensure 100% accuracy. See the paper for further explanation.

Who are the annotators?

The alignment accuracy evaluation was conducted by the authors of the paper, who have expertise in linguistics and NLP.

Personal and Sensitive Information

None

Considerations for Using the Data

Social Impact of Dataset

We hope this dataset will promote more research on the authoring process for producing abridgements, including models for automatically generating abridgements. Because it is a labor-intensive writing task, there are relatively few abridged versions of books. Systems that automatically produce abridgements could vastly expand the number of abridged versions of books and thus increase their readership.

Discussion of Biases

We present this dataset to introduce abridgement as an NLP task, but these abridgements are scoped to one small set of texts associated with a specific domain and author. There are significant practical reasons for this limited scope. In particular, in constrast to the books in AbLit, most recently published books are not included in publicly accessible datasets due to copyright restrictions, and the same restrictions typically apply to any abridgements of these books. For this reason, AbLit consists of British English literature from the 18th and 19th centuries. Some of the linguistic properties of these original books do not generalize to other types of English texts that would be beneficial to abridge. Moreover, the narrow cultural perspective reflected in these books is certainly not representative of the diverse modern population. Readers may find some content offensive.

Dataset Curators

The curators are the authors of the paper.

Licensing Information

cc-by-sa-4.0

Citation Information

Roemmele, Melissa, Kyle Shaffer, Katrina Olsen, Yiyi Wang, and Steve DeNeefe. "AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature." Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2023).