Challenge: Creating summary highlights at the sub-sentence level is desirable because sub-entrances are more concise than whole sentences.
Approach: They propose to annotate summary-worthy sub-sentences and teach classifiers to do the same . they frame task as selecting important sentences and identifying a single most informative textual unit from each sentence.
Outcome: The proposed method reduces the complexity involved in sentence compression by reducing the number of words and phrases deleted.

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Challenge: Abstractive summarizations are considered to be less reliable because they distort the original meaning and can be confusing for readers.
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Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction (2020.acl-main)

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Challenge: Sentence summarization systems that use latent space to reconstruct the source sentence are unwillingly exploited.
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HighRES: Highlight-based Reference-less Evaluation of Summarization (P19-1)

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Challenge: Existing methods for summarizing documents are inconsistent due to the difficulty of manual evaluation.
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Summary Level Training of Sentence Rewriting for Abstractive Summarization (D19-54)

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Challenge: Existing models rely on sentence-level rewards or suboptimal labels to achieve summary-level ROUGE scores.
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Exploring Content Selection in Summarization of Novel Chapters (2020.acl-main)

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Challenge: We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summary summaries.
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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.
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Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)

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Challenge: Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries.
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Deep Differential Amplifier for Extractive Summarization (2021.acl-long)

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Challenge: Existing approaches to extract summary from document with a disproportionate ratio of selected and unselected sentences are far from human performance.
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SumCSE: Summary as a transformation for Contrastive Learning (2024.findings-naacl)

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Challenge: Sentence embedding models are typically trained using contrastive learning (CL) using human annotations directly or by repurposing other annotated datasets.
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Toward Unifying Text Segmentation and Long Document Summarization (2022.emnlp-main)

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Challenge: Abstractive strategies produce more condensed summaries, but they suffer from hallucinations and factual errors, which pose a more difficult generation challenge.
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