| Challenge: | Experimental results on three news summarization datasets representative of different languages and writing styles show that our approach outperforms strong baselines by a wide margin. |
| Approach: | They propose an unsupervised approach that uses a popular ranking algorithm to compute node centrality. |
| Outcome: | The proposed approach outperforms baselines on three news summarization datasets representative of different languages and writing styles. |
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| Challenge: | Existing extractive summarization methods tend to select sentences within the same facet, which leads to facet bias. |
| Approach: | They propose a facet-aware centrality-based ranking model that gives a weight to the sentence centrality score. |
| Outcome: | The proposed method outperforms baseline models on a wide range of summarization tasks and performs comparably to other models. |
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. |
| Approach: | They propose to rank sentences using transformer attentions and pre-training objectives by unlabeled documents. |
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Learning From the Source Document: Unsupervised Abstractive Summarization (2022.findings-emnlp)
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| Challenge: | Existing methods for abstractive summarization are under supervised training, but obtaining high-quality and large-scale datasets for supervised learning is laboriously difficult. |
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Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders (2023.findings-emnlp)
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| Challenge: | Existing methods to rank sentences using pre-trained embeddings create a gap due to different optimization objectives. |
| Approach: | They propose a pre-trained embedding process that optimizes informative sentences . they use sentence-word bipartite graphs to model intra-sentential distinctive features . |
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Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking (P19-1)
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| Challenge: | Currently, unsupervised summarization is widely used for product reviews on E-commerce websites. |
| Approach: | They propose an unsupervised model that learns the latent discourse tree without an external parser and generates a concise summary. |
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Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
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Discourse-Aware Unsupervised Summarization for Long Scientific Documents (2021.eacl-main)
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| Challenge: | Existing extractive models for short news summarization are weak, despite recent advances in abstractive summarizing. |
| Approach: | They propose an unsupervised graph-based ranking model that uses a hierarchical graph representation to determine sentence importance. |
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Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction (2020.acl-main)
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| Challenge: | Sentence summarization systems that use latent space to reconstruct the source sentence are unwillingly exploited. |
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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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Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization (P18-1)
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Guokan Shang, Wensi Ding, Zekun Zhang, Antoine Tixier, Polykarpos Meladianos, Michalis Vazirgiannis, Jean-Pierre Lorré
| Challenge: | a novel graph-based framework for abstractive meeting speech summarization is developed . instead of grammatical, well-segmented sentences, the input is made of often ill-formed and ungrammatically ungrammatized text fragments called utterances. |
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