BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization (P19-1)
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| Challenge: | Abstractive summarization models are limited in size and noisy training data. |
| Approach: | They propose a bi-directional selective encoding with template model which leverages template from training data to softly select key information from each source article to guide its summarization process. |
| Outcome: | The proposed model improves the summarization performance significantly on a standard summarizing dataset. |
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| Challenge: | Existing models for abstractive summarization suffer from repetition and semantic irrelevance. |
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Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
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| Challenge: | Recent work on abstractive summarization has made progress with neural encoder-decoder architectures, but these models lack explicit semantic modeling of the source document and its summary. |
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Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization (P18-1)
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| Challenge: | Existing summarization systems rely on the source text to generate summaries, which tends to work unstably. |
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GSum: A General Framework for Guided Neural Abstractive Summarization (2021.naacl-main)
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| Challenge: | Abstractive summarization models are flexible, but they can be difficult to control. |
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| Challenge: | We present a novel system for cross-lingual summarization that can be applied to low-resource languages. |
| Approach: | They propose a neural abstractive summarization system that can be applied to low-resource languages . they use machine translation and the New York Times summarizing corpus to create a corpus . |
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Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information (2024.findings-naacl)
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| Challenge: | Prior studies have attempted to enhance faithfulness of abstractive summarization, yet hallucination remains a persistent challenge. |
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Abstractive Multi-Document Summarization via Joint Learning with Single-Document Summarization (2020.findings-emnlp)
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| Challenge: | Existing methods for document summarization are extractive and abstractive. |
| Approach: | They propose to jointly learn an abstractive single-document decoder and a decoding controller to aggregate the decoded outputs for multiple input documents. |
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Generating Summaries with Topic Templates and Structured Convolutional Decoders (P19-1)
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| Challenge: | Existing neural generation approaches create multi-sentence text as a single sequence . Existing approaches create multiple sentences as if they were a sequence based on content structure . |
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Improving Neural Machine Translation with Soft Template Prediction (2020.acl-main)
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| Challenge: | Recent advances in neural machine translation (NMT) depend on source text to generate translation. |
| Approach: | They propose to use extracted templates from tree structures as soft target templates to guide the translation procedure. |
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