Challenge: Text summarization aims to generate a short summary for an input text.
Approach: They propose a non-autoregressive unsupervised summarization approach which performs edit-based search towards a heuristicically defined score and generates a summary as pseudo-groundtruth.
Outcome: The proposed approach achieves state-of-the-art performance for unsupervised summarization, while improving inference efficiency.

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Challenge: Existing non-AutoRegressive (NAR) text generation models lack proper pre-training, making them far behind pre-trained autoregressive models.
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Non-Autoregressive Sentence Ordering (2023.findings-emnlp)

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Challenge: Existing sentence ordering approaches only leverage unilateral dependencies during decoding and cannot fully explore the semantic dependency between sentences.
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Non-Autoregressive Text Generation with Pre-trained Language Models (2021.eacl-main)

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Challenge: Autoregressive generation models generate tokens in a left-to-right, token-by-token fashion, resulting in lag in inference.
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Simple Unsupervised Summarization by Contextual Matching (P19-1)

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Challenge: Existing methods for sentence summarization require a large amount of parallel data for supervision to work.
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TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising (2020.findings-emnlp)

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Challenge: Existing abstractive summarization models ignore abundant unlabeled corpora resources . TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets .
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JANUS: Joint Autoregressive and Non-autoregressive Training with Auxiliary Loss for Sequence Generation (2022.emnlp-main)

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Challenge: Existing approaches to train autoregressive and non-autoregressive models only consider relevance of model parameters, ignoring correlations between the two manners.
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Unsupervised Extractive Opinion Summarization Using Sparse Coding (2022.acl-long)

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Challenge: Existing methods for opinion summarization rely on human annotations, which may not be feasible.
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Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization (2022.findings-emnlp)

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Challenge: Existing models to summarize texts without ground-truth summaries are extractive, which remove words from texts and thus are less flexible than abstractive models.
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Unsupervised Opinion Summarization with Noising and Denoising (2020.acl-main)

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Challenge: Existing methods for abstractive summarization are limited and cannot be easily sourced.
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Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers (2020.findings-emnlp)

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Challenge: Existing methods for document summarization use graphs and unlabeled documents . Existing models require labeled data, and it is expensive to create summarized documents.
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