| Challenge: | Abstractive summarization (ABS) has gained overwhelming success owing to a tremendous development of sequence-to-sequence models and its variants. |
| Approach: | They propose a concept pointer network that leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts and then points to the most appropriate choice using both the concept set and original source text. |
| Outcome: | The proposed model improves on the DUC-2004 and Gigaword datasets and human evaluation of its abstractive abilities supports the quality of the summaries produced. |
Similar Papers
Abstractive Summarizers are Excellent Extractive Summarizers (2023.acl-short)
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| Challenge: | Abstractive summarization systems have traditionally been fragmented, limiting the benefits of compatible models. |
| Approach: | They propose three new inference algorithms using sequence-to-sequence architectures to model extractive summarization with an abstractive summmarization system. |
| Outcome: | The proposed algorithms outperform existing models on CNN and Dailymail and show that they are more efficient than existing models. |
Unsupervised Semantic Abstractive Summarization (P18-3)
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| Challenge: | Existing methods for abstractive summarization are limited in the sense that they can never generate human level summaries for large and complicated documents. |
| Approach: | They propose a pipeline for automatic abstractive summary generation using co-reference resolution and Meta Nodes. |
| Outcome: | The proposed pipeline outperforms the state-of-the-art method by 1.7% in node prediction. |
Controlling Length in Abstractive Summarization Using a Convolutional Neural Network (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) can't generate summaries of desired lengths due to space or length constraints. |
| Approach: | They propose an approach to constrain the summary length by extending a convolutional sequence to sequence model. |
| Outcome: | The proposed model outperforms baseline models in terms of ROUGE score, length variations and semantic similarity. |
To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text (2021.findings-acl)
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| Challenge: | Abstractive summarization models have seen great improvements in recent years, but there is limited understanding of the strategies different models employ and how they relate their understanding of language. |
| Approach: | They characterize how one popular abstractive model uses an explicit copy/generation switch to control its level of abstraction vs extraction . they find that abstractive summarization models lack the semantic understanding necessary to generate paraphrases that are both abstractive and faithful to the source document. |
| Outcome: | The proposed model uses syntactic boundaries to truncate sentences that are often copied verbatim. |
Topic-Guided Abstractive Multi-Document Summarization (2021.findings-emnlp)
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| Challenge: | Existing studies on multi-document summarization (MDS) focus on extractive and abstractive approaches to create a fluent and concise summary for a collection of thematically related documents. |
| Approach: | They propose a novel abstractive MDS model that represents multiple documents as a heterogeneous graph and then applies a graph-to-sequence framework to generate summaries. |
| Outcome: | The proposed model outperforms state-of-the-art models on Rouge scores and human evaluation, while learning high-quality topics. |
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing (D19-1)
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| Challenge: | Extensive experiments on a large-scale real-world text summarization dataset show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations. |
| Approach: | They propose a model that learns summary patterns and prototype facts from a prototype document . they use a fact checker to estimate mutual information between the input document and generated summary . |
| Outcome: | Experiments on a large-scale real-world text summarization dataset show that PESG achieves state-of-the-art performance. |
Leveraging Graph to Improve Abstractive Multi-Document Summarization (2020.acl-main)
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| Challenge: | Empirical results show that our model brings substantial improvements over several strong baselines. |
| Approach: | They propose a neural abstractive multi-document summarization model which captures cross-document relations and can guide the summary generation process. |
| Outcome: | The proposed model improves on the WikiSum and MultiNews datasets and can be easily combined with pre-trained language models. |
Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator (D19-1)
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| Challenge: | Modern pointer generators only capture exact word matches, ignoring possible inflections or abstractions, which restricts its power of capturing richer latent alignment. |
| Approach: | They propose a pointer generator architecture that allows the model to "edit" pointed tokens instead of always copying them. |
| Outcome: | The proposed model captures more latent alignment relations than exact word matches and generates higher-quality summaries validated by both qualitative and quantitative evaluations. |
On the Abstractiveness of Neural Document Summarization (D18-1)
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| Challenge: | Recent studies show that document summarization systems are abstractive . authors suggest that automated summarizing systems could be improved . |
| Approach: | They propose to use a pure copy system to verify abstractiveness of document summarization systems. |
| Outcome: | The proposed system produces abstractive summaries while being far more efficient. |
Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization (2020.acl-main)
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| Challenge: | Existing methods for document summarization use extractive and abstractive representations, but they don't take into account hierarchical structure of document clusters. |
| Approach: | They propose a multi-granularity interaction network for extractive and abstractive multi-document summarization which jointly learn semantic representations for words, sentences, and documents. |
| Outcome: | The proposed model outperforms baseline methods and achieves the best results on the Multi-News dataset. |