| 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. |
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A New Annotated Portuguese/Spanish Corpus for the Multi-Sentence Compression Task (L18-1)
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| Challenge: | Existing corpus for Multi-sentence Compression (MSC) tasks is limited to English . a dataset is available for MSC tasks in the French language . |
| Approach: | They propose a new corpus for Multi-Sentence Compression task in Portuguese and Spanish. |
| Outcome: | The proposed corpus is compared with two state-of-the-art systems in Portuguese and Spanish. |
Sentence Compression for Arbitrary Languages via Multilingual Pivoting (D18-1)
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| Challenge: | a new study advocates the use of bilingual corpora for sentence compression models . previous work focused on word deletion, while others view sentence compression as a general text rewriting problem. |
| Approach: | They propose to use bilingual corpora for training sentence compression models. |
| Outcome: | The proposed model can be trained for any language as long as a bilingual corpus is available . it performs arbitrary rewrites without access to compression specific data . |
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. |
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. |
An Operation Network for Abstractive Sentence Compression (C18-1)
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| 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. |
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. |
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. |
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. |
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation (2021.naacl-srw)
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| Challenge: | Neural machine translation is known to show poor performance at long sentence translations . however, when the sentence length exceeds a certain value, the quality of NMT becomes inferior to that of statistical machine translation. |
| Approach: | They propose a method that uses given parallel corpora as train data to generate long sentences by concatenating two sentences at random. |
| Outcome: | The proposed method improves translation quality more when combined with back-translation. |
Multi-Word Lexical Simplification (2020.coling-main)
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| Challenge: | In text simplification, individual words are replaced with their simpler equivalents, but single word substitutions do not cover the full complexity of techniques humans use to approach text simulating. |
| Approach: | They propose a task of multi-word lexical simplification in which a sentence is made easier to understand by replacing its fragment with a simpler alternative. |
| Outcome: | The proposed method is based on a purpose-trained neural language model and evaluates against human and resource-based baselines. |