Iterative Edit-Based Unsupervised Sentence Simplification

• URL: http://arxiv.org/abs/2006.09639v1
• Date: Wed, 17 Jun 2020 03:53:12 GMT
• Title: Iterative Edit-Based Unsupervised Sentence Simplification
• Authors: Dhruv Kumar, Lili Mou, Lukasz Golab, Olga Vechtomova
• Abstract summary: Our model is guided by a scoring function involving fluency, simplicity, and meaning preservation. We iteratively perform word and phrase-level edits on the complex sentence.
• Score: 30.128553647491817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel iterative, edit-based approach to unsupervised sentence simplification. Our model is guided by a scoring function involving fluency, simplicity, and meaning preservation. Then, we iteratively perform word and phrase-level edits on the complex sentence. Compared with previous approaches, our model does not require a parallel training set, but is more controllable and interpretable. Experiments on Newsela and WikiLarge datasets show that our approach is nearly as effective as state-of-the-art supervised approaches.

Related papers

• A Simple and Plug-and-play Method for Unsupervised Sentence Representation Enhancement [35.6803390044542]
  RepAL is an extremely simple post-processing method that enhances sentence representations. We show that RepAL is free of training and is a plug-and-play method that can be combined with most existing unsupervised sentence learning models.
  arXiv  Detail & Related papers  (2023-05-13T02:43:59Z)
• Sentence Representation Learning with Generative Objective rather than Contrastive Objective [86.01683892956144]
  We propose a novel generative self-supervised learning objective based on phrase reconstruction. Our generative learning achieves powerful enough performance improvement and outperforms the current state-of-the-art contrastive methods.
  arXiv  Detail & Related papers  (2022-10-16T07:47:46Z)
• Text Revision by On-the-Fly Representation Optimization [76.11035270753757]
  Current state-of-the-art methods formulate these tasks as sequence-to-sequence learning problems. We present an iterative in-place editing approach for text revision, which requires no parallel data. It achieves competitive and even better performance than state-of-the-art supervised methods on text simplification.
  arXiv  Detail & Related papers  (2022-04-15T07:38:08Z)
• GRS: Combining Generation and Revision in Unsupervised Sentence Simplification [7.129708913903111]
  We propose an unsupervised approach to sentence simplification that combines text generation and text revision. We start with an iterative framework in which an input sentence is revised using explicit edit operations, and add paraphrasing as a new edit operation. This allows us to combine the advantages of generative and revision-based approaches: paraphrasing captures complex edit operations, and the use of explicit edit operations in an iterative manner provides controllability and interpretability.
  arXiv  Detail & Related papers  (2022-03-18T04:52:54Z)
• The Summary Loop: Learning to Write Abstractive Summaries Without Examples [21.85348918324668]
  This work presents a new approach to unsupervised abstractive summarization based on maximizing a combination of coverage and fluency for a given length constraint. Key terms are masked out of the original document and must be filled in by a coverage model using the current generated summary. When tested on popular news summarization datasets, the method outperforms previous unsupervised methods by more than 2 R-1 points.
  arXiv  Detail & Related papers  (2021-05-11T23:19:46Z)
• Narrative Incoherence Detection [76.43894977558811]
  We propose the task of narrative incoherence detection as a new arena for inter-sentential semantic understanding. Given a multi-sentence narrative, decide whether there exist any semantic discrepancies in the narrative flow.
  arXiv  Detail & Related papers  (2020-12-21T07:18:08Z)
• 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)
• Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers [107.12125265675483]
  Unsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training. Existing methods are mostly graph-based with sentences as nodes and edge weights measured by sentence similarities. We find that transformer attentions can be used to rank sentences for unsupervised extractive summarization.
  arXiv  Detail & Related papers  (2020-10-16T08:44:09Z)
• 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)

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.