Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu
| Challenge: | Abstractive summarization systems struggle to combine information from multiple sources, resulting in poor grammar and incorrect facts. |
| Approach: | They analyze the outputs of five abstractive summarization systems and examine their grammatical accuracy and faithfulness. |
| Outcome: | The proposed summarization systems are able to combine information from multiple sources, but they often fail to remain faithful to the original document. |
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Scoring Sentence Singletons and Pairs for Abstractive Summarization (P19-1)
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Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu
| Challenge: | Existing methods for summarizing content from single sentences are inadequately understood. |
| Approach: | They propose to combine singletons and pairs to create a summarizing sentence . they use a dataset of human-written abstracts to examine human-writing methods . |
| Outcome: | The proposed framework is based on human-written abstracts from three large datasets. |
Learning to Fuse Sentences with Transformers for Summarization (2020.emnlp-main)
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| Challenge: | Abstractive summarization systems that fuse sentences are not rewarded for correctly fusing sentences. |
| Approach: | They propose to leverage the knowledge of points of correspondence between sentences to enhance their ability to fuse sentences. |
| Outcome: | The proposed algorithms improve the ability of the proposed summarization systems to fuse sentences and show that they can fuse sentences in a way that retains the original meaning. |
Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion (C18-1)
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| Challenge: | a new method for abstractive summarization is being developed for document summarizing . abstractive methods require extensive natural language generation to rewrite the sentences . |
| Approach: | They propose an unsupervised abstractive summarization system in multi-document context . they use a paraphrastic sentence fusion model which performs sentence synthesis and paraphrazing . |
| Outcome: | The proposed model improves information coverage and abstractiveness of generated sentences. |
Source Identification in Abstractive Summarization (2024.eacl-short)
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| Challenge: | Existing studies define input sentences that contain essential information in the generated summary as source sentences. |
| Approach: | They define input sentences that contain essential information in the generated summary as source sentences and analyze the source sentences to determine how abstractive summaries are made. |
| Outcome: | The proposed method performs well in abstractive settings, while similarity-based methods perform robustly in extractive settings. |
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. |
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. |
Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation (2022.aacl-main)
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Abhay Shukla, Paheli Bhattacharya, Soham Poddar, Rajdeep Mukherjee, Kripabandhu Ghosh, Pawan Goyal, Saptarshi Ghosh
| Challenge: | Summarization of legal case judgement documents is a challenging problem in Legal NLP. |
| Approach: | They propose to use extractive and abstractive summarization methods to evaluate legal document summarizing systems. |
| Outcome: | The proposed methods have been evaluated on three legal summarization datasets. |
Abstractive Document Summarization without Parallel Data (2020.lrec-1)
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| Challenge: | Abstractive summarization typically relies on large collections of paired articles and summaries. |
| Approach: | They propose a system that relies only on example summaries and non-matching articles . they use an unsupervised sentence extractor that selects salient sentences . |
| Outcome: | The proposed system performs well on CNN/DailyMail benchmark and automatic generating a press release from a scientific journal article. |
Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples (N19-1)
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| Challenge: | Neural abstractive summarization systems generate summary texts conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarizing datasets. |
| Approach: | They propose to analyze existing neural abstractive summarization systems by comparing their performance to human-written summaries. |
| Outcome: | The proposed systems perform better than human-written summarizations on different datasets and show that they are able to understand deeper syntactic and semantic structures. |
Proceedings of the 2nd Workshop on New Frontiers in Summarization (D19-54)
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| Challenge: | EMNLP 2017 is a workshop on enhancing natural language processing's ability to produce concise, fluent summaries. |
| Approach: | the workshop provides a forum for cross-fertilization of ideas towards automatic summarization . four invited speakers will be present at the workshop . |
| Outcome: | the workshop aims to provide a forum for cross-fertilization of ideas towards automatic summarization. |