| Challenge: | Experimental results show that system summaries struggle to preserve syntactic meaning of source texts. |
| Approach: | They propose to incorporate syntactic information from source sentences into abstractive summaries by structure-infused copy mechanisms. |
| Outcome: | The proposed approach compares favorably to state-of-the-art methods. |
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StructSum: Summarization via Structured Representations (2021.eacl-main)
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Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov
| Challenge: | Abstractive summarization models overfit to training corpora, lack of transparency and layout bias . authors propose incorporating latent and explicit dependencies across sentences in source document . |
| Approach: | They propose a framework based on document-level structure induction to address layout bias and lack of transparency in abstractive summarization models. |
| Outcome: | The proposed framework improves coverage of content in the source documents and generates more abstractive summaries by generating more novel n-grams. |
Self-Attention Guided Copy Mechanism for Abstractive Summarization (2020.acl-main)
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| Challenge: | Abstractive summarization models have been widely used to extract words from source into summary, but how to ensure that important words in source are copied remains a challenge. |
| Approach: | They propose a Transformer-based model to enhance copy mechanism by identifying the importance of each source word based on the degree centrality. |
| Outcome: | The proposed model outperforms baseline methods on CNN/Daily Mail and Gigaword datasets. |
Analyzing Sentence Fusion in Abstractive Summarization (D19-54)
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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. |
Inducing Document Structure for Aspect-based Summarization (P19-1)
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| Challenge: | Abstractive summarization systems treat documents as unstructured and generate a single generic summary per document. |
| Approach: | They propose to incorporate document structure into automatic summarization systems . they induce latent document structure and abstractive summarizing objective . |
| Outcome: | The proposed model improves on topic-agnostic baselines and can produce abstractive and extractive aspect-based summaries. |
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. |
Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization (2021.emnlp-main)
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| Challenge: | Existing methods for abstractive summarization use encoder-decoder attention, but this leads to incomplete copying. |
| Approach: | They propose a copying scheme that takes advantage of prior copying distributions and explicitly encourages the model to copy the input word that is relevant to the previously copied one. |
| Outcome: | The proposed scheme achieves state-of-the-art on summarization benchmarks . it takes advantage of prior copying distributions and explicitly encourages copying . |
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. |
| Outcome: | The proposed model outperforms six state-of-the-art methods on two benchmark corpus datasets. |
BASS: Boosting Abstractive Summarization with Unified Semantic Graph (2021.acl-long)
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| Challenge: | Abstractive summarization for long-document or multi-document remains challenging for Seq2Seq as it does not analyze long-distance relations in text. |
| Approach: | They propose a framework for Boosting Abstractive Summarization based on a unified Semantic graph which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases. |
| Outcome: | The proposed framework improves document representation and summary generation process by leveraging the graph structure. |
Contrastive Attention Mechanism for Abstractive Sentence Summarization (D19-1)
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| Challenge: | Existing attention mechanisms for abstractive sentence summarization are based on rule-based methods and large-scale training corpora. |
| Approach: | They propose a contrastive attention mechanism that extends the sequence-to-sequence framework for abstractive sentence summarization task. |
| Outcome: | The proposed mechanism improves the state-of-the-art on the abstractive sentence summarization task. |
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. |