Which Scores to Predict in Sentence Regression for Text Summarization?

Zopf, Markus; Mencía, Eneldo Loza; Fürnkranz, Johannes

doi:10.18653/v1/n18-1161

Cited by 11 publications

(5 citation statements)

References 21 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…First, we create a binary label for each sentence indicating whether it belongs in the summary (Gillick et al, 2008). 6 We compute a Rouge-2 Precision score of a sentence relative to the reference summary and simplify it to a binary value based on whether it is above or below 0.1 (Lin, 2004;Zopf et al, 2018). As an example, the sentences in the positive class are highlighted in green in Figure 2.…”

Section: Benchmark Methodsmentioning

confidence: 99%

BillSum: A Corpus for Automatic Summarization of US Legislation

Kornilova¹,

Eidelman²

2019

Proceedings of the 2nd Workshop on New Frontiers in Summarization

View full text Add to dashboard Cite

Automatic summarization methods have been studied on a variety of domains, including news and scientific articles. Yet, legislation has not previously been considered for this task, despite US Congress and state governments releasing tens of thousands of bills every year.In this paper, we introduce BillSum, the first dataset for summarization of US Congressional and California state bills (https://github.com/ FiscalNote/BillSum). We explain the properties of the dataset that make it more challenging to process than other domains. Then, we benchmark extractive methods that consider neural sentence representations and traditional contextual features. Finally, we demonstrate that models built on Congressional bills can be used to summarize California bills, thus, showing that methods developed on this dataset can transfer to states without human-written summaries.

show abstract

Section: Benchmark Methodsmentioning

confidence: 99%

BillSum: A Corpus for Automatic Summarization of US Legislation

Kornilova¹,

Eidelman²

2019

Proceedings of the 2nd Workshop on New Frontiers in Summarization

View full text Add to dashboard Cite

show abstract

“…A supervised approach has been taken for extractive summarization (Collins et al, 2017 ), and to extract essential sentences, regression was used as sentence scoring (Zopf et al, 2018 ).…”

Section: Related Workmentioning

confidence: 99%

SATS: simplification aware text summarization of scientific documents

Zaman,

Kamiran,

Shardlow

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

Simplifying summaries of scholarly publications has been a popular method for conveying scientific discoveries to a broader audience. While text summarization aims to shorten long documents, simplification seeks to reduce the complexity of a document. To accomplish these tasks collectively, there is a need to develop machine learning methods to shorten and simplify longer texts. This study presents a new Simplification Aware Text Summarization model (SATS) based on future n-gram prediction. The proposed SATS model extends ProphetNet, a text summarization model, by enhancing the objective function using a word frequency lexicon for simplification tasks. We have evaluated the performance of SATS on a recently published text summarization and simplification corpus consisting of 5,400 scientific article pairs. Our results in terms of automatic evaluation demonstrate that SATS outperforms state-of-the-art models for simplification, summarization, and joint simplification-summarization across two datasets on ROUGE, SARI, and CSS1. We also provide human evaluation of summaries generated by the SATS model. We evaluated 100 summaries from eight annotators for grammar, coherence, consistency, fluency, and simplicity. The average human judgment for all evaluated dimensions lies between 4.0 and 4.5 on a scale from 1 to 5 where 1 means low and 5 means high.

show abstract

“…More recently extractive summarisation has been used in a supervised manner (Collins et al, 2017), especially at the sentence level. One method of extracting sentences is sentence regression (Zopf, Loza Mencía, & Fürnkranz, 2018), which predicts supervised utility scores at the sentence level. Extractive summarisation has also been performed using deep learning (Kinugawa & Tsuruoka, 2017).…”

Section: Extractive Summarisationmentioning

confidence: 99%