Neural Document Summarization by Jointly Learning to Score and Select Sentences (P18-1)
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| Challenge: | Sentence scoring and sentence selection are two main steps in extractive document summarization systems. |
| Approach: | They propose an end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences. |
| Outcome: | The proposed framework outperforms the state-of-the-art summarization models on the CNN/Daily Mail dataset. |
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| Challenge: | Recent approaches to summarization are either selection-based extraction or generation-based abstraction. |
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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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Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks (2020.coling-main)
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| Challenge: | Existing extractive summarization models hardly capture inter-sentence relationships, especially in long documents. |
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A Set Prediction Network For Extractive Summarization (2023.findings-acl)
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| Challenge: | Recent approaches to extracting salient sentences from source document are naive and lack dependencies between sentences. |
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Extractive Summarization as Text Matching (2020.acl-main)
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| Challenge: | Currently, most of the neural extractive summarization systems score and extract sentences individually and model the relationship between sentences. |
| Approach: | They propose to instantiate a neural extractive summarization task as a semantic text matching problem and use it to match a source document and candidate summaries in a semantic space. |
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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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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. |
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization (2021.emnlp-main)
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| Challenge: | Sentence-level extractive text summarization is difficult to model the importance of sentences. |
| Approach: | They propose a Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization that leverages Frame semantics to model sentences from both intra-sentence level and inter-sentent level. |
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Ranking Sentences for Extractive Summarization with Reinforcement Learning (N18-1)
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| Challenge: | Abstractive summarization involves various text rewriting operations and has been identified as a sequence-to-sequence problem. |
| Approach: | They propose a novel algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. |
| Outcome: | The proposed algorithm outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans. |
Neural Latent Extractive Document Summarization (D18-1)
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| Challenge: | Existing summarization paradigms focus on extractive summarizing based on sentence level labels . |
| Approach: | They propose a latent variable extractive model where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. |
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