| Challenge: | Existing approaches to summarize text using end-to-end content selectors have had mixed success in content selection, for example copying full sentences from the source document. |
| Approach: | They propose to use content selectors to over-determine phrases in a source document that should be part of the summary. |
| Outcome: | The proposed model over-determines phrases in a source document that should be part of the summary while generating fluent summaries. |
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| Challenge: | Recent models infer latent representations of words or tokens with a transformer encoder, which is bottom-up and thus does not capture long-distance context well. |
| Approach: | They propose a method to infer latent representations of words or tokens in documents . they assume a hierarchical structure of a document where top-level captures long range dependency . |
| Outcome: | The proposed model can summarize an entire book and achieve competitive performance on a wide range of document summarization benchmarks. |
Long-Span Summarization via Local Attention and Content Selection (2021.acl-long)
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| Challenge: | Transformer-based models are state-of-the-art for a wide range of natural language processing tasks, including document summarization. |
| Approach: | They exploit large pre-trained transformer-based models and address long-span dependencies in abstractive summarization using two methods: local self-attention; and explicit content selection. |
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Content Selection in Deep Learning Models of Summarization (D18-1)
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| Challenge: | Using deep learning models, we find that word embedding does not improve performance over simpler models. |
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Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (D18-1)
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| Challenge: | Existing approaches to summarize documents are not extractive and require an abstractive approach. |
| Approach: | They propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. |
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Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)
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| Challenge: | Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. |
| Approach: | They propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. |
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Jointly Extracting and Compressing Documents with Summary State Representations (N19-1)
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| Challenge: | Text summarization is an important NLP problem with a wide range of applications in data-driven industries. |
| Approach: | They propose a neural model that extracts sentences from a document and compresses them. |
| Outcome: | The proposed model generates concise and informa-tive summaries on CNN/DailyMail and Newsroom datasets and human evaluations show it outperforms existing methods. |
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. |
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Learning to Prioritize: Precision-Driven Sentence Filtering for Long Text Summarization (2022.lrec-1)
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| Challenge: | Neural text summarization models are limited by their maximum input length, posing a challenge to summarizing longer texts comprehensively. |
| Approach: | They propose a pre-processing layer that removes low-quality sentences in articles to improve existing summarization models. |
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Enriching and Controlling Global Semantics for Text Summarization (2021.emnlp-main)
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| Challenge: | Abstractive summarization models have been proven effective in creating fluent and informative summaries, but they suffer from the short-range dependency problem, causing them to produce summary that miss the key points of document. |
| Approach: | They propose a neural topic model empowered with normalizing flow to capture global semantics of the document and integrate them into the summarization model. |
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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. |