| Challenge: | Sentence compression is a natural language generation task that condenses a sentence . Delete-based models remove unimportant words from the source sentence and generate a shorter sentence if the source is not a word deletion problem. |
| Approach: | They propose a neural network approach for abstractive sentence compression . they model the sentence compression process as an editing procedure . |
| Outcome: | The proposed approach outperforms state-of-the-art models in the abstractive sentence compression field. |
Similar Papers
A Language Model based Evaluator for Sentence Compression (P18-2)
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| Challenge: | Existing methods to produce readable sentence compression are based on machine learning or syntactic tree-based approaches. |
| Approach: | They propose a language-model-based evaluator for deletion-based sentence compression . they propose deleting operations on source sentences to obtain best target compression based on the proposed model . |
| Outcome: | The proposed model generates more readable compression comparable to strong baselines. |
A Simple Yet Effective Corpus Construction Method for Chinese Sentence Compression (2022.lrec-1)
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| Challenge: | Deletion-based sentence compression has made significant progress in the english language . however, there is a lack of large-scale and high-quality parallel corpus for the Chinese language to train an efficient system. |
| Approach: | They propose to construct a Chinese corpus with 151k pairs of sentences and train extractive and generative neural compression models on the constructed corpus. |
| Outcome: | The proposed method generates high-quality compressed sentences on automatic and human evaluation metrics compared with baselines. |
Unsupervised Rewriter for Multi-Sentence Compression (P19-1)
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| Challenge: | Multi-sentence compression aims to generate a grammatical but reduced compression from multiple input sentences while retaining key information. |
| Approach: | They propose a neural rewriter for multi-sentence compression that does not need any parallel corpus. |
| Outcome: | Empirical studies show that the proposed approach achieves comparable results upon automatic evaluation and improves the grammaticality of compression based on human evaluation. |
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing (P19-1)
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| Challenge: | Current sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. |
| Approach: | They propose a sentence simplification model that learns explicit edit operations via a neural programmer-interpreter approach. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, -1.41 Newsela) |
Neural Extractive Text Summarization with Syntactic Compression (D19-1)
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| Challenge: | Recent approaches to summarization are either selection-based extraction or generation-based abstraction. |
| Approach: | They propose a neural model for single-document summarization based on joint extraction and syntactic compression. |
| Outcome: | The proposed model outperforms an off-the-shelf compression module and its output generally remains grammatical. |
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning (2022.acl-long)
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| Challenge: | Recent unsupervised sentence compression approaches use custom objectives to guide discrete search, but guided search is expensive at inference time. |
| Approach: | They propose to use reinforcement learning to train effective sentence compression models that are also fast when generating predictions. |
| Outcome: | The proposed model outperforms other unsupervised models while being faster at inference time. |
SCAR: Sentence Compression using Autoencoders for Reconstruction (2020.acl-srw)
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| Challenge: | Sentence compression is the task of shortening a sentence while retaining its meaning. |
| Approach: | They propose to use a supervised deep learning framework to shorten sentences while retaining their meaning by a compressor and reconstructor. |
| Outcome: | The proposed model achieves higher ROUGE scores on benchmark datasets than the existing state-of-the-art methods and baselines. |
SEQˆ3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression (N19-1)
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| Challenge: | Neural sequence-to-sequence models are currently the dominant approach in natural language processing tasks, but require massive parallel corpora. |
| Approach: | They propose a sequence-to-sequence-tosequnce autoencoder with words as latent variables . they apply the model to unsupervised abstractive sentence compression . |
| Outcome: | The proposed model achieves promising results in unsupervised sentence compression on benchmark datasets. |
Controllable Sentence Simplification via Operation Classification (2022.findings-naacl)
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| Challenge: | Sentence simplification involves a sentence being transformed into a simpler version of itself while preserving its core meaning. |
| Approach: | They propose a controllable-simplification model that tailors simplifications to four global operations . they propose to use a dataset to train highly accurate classification systems for these operations based on syntactic or discourse structure . |
| Outcome: | The proposed model outperforms both end-to-end and controllable approaches in sentence simplification tasks. |
Sentence Simplification with Memory-Augmented Neural Networks (N18-2)
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| Challenge: | Sentence simplification aims to simplify the content and structure of complex sentences . prior work has focused on monolingual machine translation (MT) and tree-based MT (TBMT). |
| Approach: | They adapt an architecture with augmented memory capacities called Neural Semantic Encoders for sentence simplification. |
| Outcome: | The proposed architecture improves on different datasets and improves human judgments. |