# Abstractive vs. Extractive Summarization: An Experimental Review
## 2 Related Work
### Datasets  
This subsection reports the selected evaluation datasets and their characteristics (an overview of them is given in Table 1). We opted for multiple datasets, aiming to test how well the approaches generalize over different data. _CNN/Daily Mail_[54] is a dataset containing over 300,000 news articles from _CNN_ and the _Daily Mail_ newspaper, written between 2007 and 2015. This dataset is distributed in three major versions. The first one was made for the NLP task of question answering and contains 313 k unique news articles and close to 1 M questions. The second version was restructured for the task of TS; the data in this version are anonymized. The third version provides a nonanonymized version of the data, where individuals' names can be found in the dataset. Each article is accompanied by a list of bullet point summaries, which abstractively summarize a key aspect of the article. The _CNN/Daily Mail_ dataset has 3 splits: training (92%, 287,113 articles), validation (4.3% 13,368 articles), and test (3.7%, 11,490 articles). _XSum_ (standing for _eXtreme Summarization_) is a dataset that provides over 220,000 _BBC_ news articles covering various topics [8]. Each article is accompanied by a one-sentence summary written by a human expert, who for the most part was the original author of the article. _XSum_ has 3 splits: training (90%, 204,045 articles), validation (5%, 11,332 articles), and test (5%, 11,334 articles). _SAMSum_[55] is a dataset that contains more than 16,000 online chat conversations written by linguists. These conversations cover diverse topics and formality styles including emoticons, slang words, and even typographical errors. They are also annotated with short third-person summaries, explaining the dialogue between different people. _Reddit TIFU_[56] is a dataset consisting of 123,000 Reddit posts from the _/r/ifu_ online discussion forum. These posts are informal stories that include a short summary, which is the title of the post, and a longer one, known as the _\"TL;DR\"_ (_too long; didn't read_) summary. _BillSum_[57] is a dataset that deals with US Congressional (USC) and California (CA) state bill summarization. This corpus contains legislation documents from five to twenty thousand characters. In total, it contains 22,200 USC (18,949 train documents and 3269 test documents) and 1200 CA state bills (1237 test documents), accompanied by summaries written by human experts. The data are collected from the US Publishing Office _Govinfo_ and the CA legislature's website. _XLSum_[58] is a multilingual dataset that covers 44 different languages and offers more than 1 million pairs of BBC news articles and their summaries. Since our review focuses on  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Size**} & \multicolumn{2}{c}{**Mean \#Words -**} \\  & & **Mean \#Sentences** & **Link** \\  & & **(Text/Summary)** & \\ \hline \multirow{3}{*}{_CNN/Daily Mail_} & \multirow{3}{*}{312 k News} & \multirow{3}{*}{766/53 words--} & [[https://github.com/abisee/cnn-dailymail](https://github.com/abisee/cnn-dailymail)]([https://github.com/abisee/cnn-dailymail](https://github.com/abisee/cnn-dailymail)) \\  & & & (accessed on 19 June 2023) \\  & & Articles & 29.74/3.72 sentences & [[https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail](https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail)]([https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail](https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail)) (accessed on  
summarization approaches that were trained and finetuned in the English language, we utilize the English part of the dataset which has 3 splits: training (93%, 306,522 articles), validation (3.5%, 11,535 articles), and test (3.5%, 11,535 articles). The authors of _XLSum_ argue that they provide a more concise dataset, with less irrelevant summaries compared to _CNN/Daily Mail_ and _XSum_ datasets.