SASS: Data and Methods for Subject Aware Sentence Simplification

• URL: http://arxiv.org/abs/2303.14589v1
• Date: Sun, 26 Mar 2023 00:02:25 GMT
• Title: SASS: Data and Methods for Subject Aware Sentence Simplification
• Authors: Brad Windsor, Luke Martin, Anand Tyagi
• Abstract summary: This paper provides a dataset aimed at training models that perform subject aware sentence simplifications. We also test models on that dataset which are inspired by model architecture used in abstractive summarization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sentence simplification tends to focus on the generic simplification of sentences by making them more readable and easier to understand. This paper provides a dataset aimed at training models that perform subject aware sentence simplifications rather than simplifying sentences as a whole. We also test models on that dataset which are inspired by model architecture used in abstractive summarization. We hand generated portions of the data and augment the dataset by further manipulating those hand written simplifications. Our results show that data-augmentation, data-masking, and model architecture choices used in summarization provide a solid baseline for comparison on subject aware simplification.

Related papers

• Exploiting Summarization Data to Help Text Simplification [50.0624778757462]
  We analyzed the similarity between text summarization and text simplification and exploited summarization data to help simplify. We named these pairs Sum4Simp (S4S) and conducted human evaluations to show that S4S is high-quality.
  arXiv  Detail & Related papers  (2023-02-14T15:32:04Z)
• Document-Level Text Simplification: Dataset, Criteria and Baseline [75.58761130635824]
  We define and investigate a new task of document-level text simplification. Based on Wikipedia dumps, we first construct a large-scale dataset named D-Wikipedia. We propose a new automatic evaluation metric called D-SARI that is more suitable for the document-level simplification task.
  arXiv  Detail & Related papers  (2021-10-11T08:15:31Z)
• SDA: Improving Text Generation with Self Data Augmentation [88.24594090105899]
  We propose to improve the standard maximum likelihood estimation (MLE) paradigm by incorporating a self-imitation-learning phase for automatic data augmentation. Unlike most existing sentence-level augmentation strategies, our method is more general and could be easily adapted to any MLE-based training procedure.
  arXiv  Detail & Related papers  (2021-01-02T01:15:57Z)
• Controllable Text Simplification with Explicit Paraphrasing [88.02804405275785]
  Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously. We propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles.
  arXiv  Detail & Related papers  (2020-10-21T13:44:40Z)
• Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification [33.08519864889526]
  We present the first data-driven study of content addition in text simplification. We analyze how entities, ideas, and concepts are elaborated through the lens of contextual specificity. Our results illustrate the complexities of elaborative simplification, suggesting many interesting directions for future work.
  arXiv  Detail & Related papers  (2020-10-20T05:06:23Z)
• ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations [97.27005783856285]
  This paper introduces ASSET, a new dataset for assessing sentence simplification in English. We show that simplifications in ASSET are better at capturing characteristics of simplicity when compared to other standard evaluation datasets for the task.
  arXiv  Detail & Related papers  (2020-05-01T16:44:54Z)
• MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases [20.84836431084352]
  We introduce MUSS, a Multilingual Unsupervised Sentence Simplification system that does not require labeled simplification data. MUSS uses a novel approach to sentence simplification that trains strong models using sentence-level paraphrase data instead of proper simplification data. We evaluate our approach on English, French, and Spanish simplification benchmarks and closely match or outperform the previous best supervised results.
  arXiv  Detail & Related papers  (2020-05-01T12:54:30Z)
• Extractive Summarization as Text Matching [123.09816729675838]
  This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. We formulate the extractive summarization task as a semantic text matching problem. We have driven the state-of-the-art extractive result on CNN/DailyMail to a new level (44.41 in ROUGE-1)
  arXiv  Detail & Related papers  (2020-04-19T08:27:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.