Results 61 to 70 of about 4,298,648 (328)
Experiences with and Reflections on Text Summarization Tools [PDF]
International Journal of Computational Intelligence Systems, 2009 Text summarization is a process of distilling the most important content from text documents. While human beings have proven to be extremely capable summarizers, computer based automatic abstracting and summarizing has proven to be extremely challenging ...
Shuha Liu
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Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer
Conference on Empirical Methods in Natural Language Processing, 2021 Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents. However, constructing a coherent and informative summary is difficult using a pre-trained BERT-based encoder since it is ...
Jingun Kwon +3 more
semanticscholar +1 more source
Review of automatic text summarization techniques & methods
Journal of King Saud University: Computer and Information Sciences, 2022 Text summarization automatically produces a summary containing important sentences and includes all relevant important information from the original document.
Adhika Pramita Widyassari +6 more
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AAAI Conference on Artificial Intelligence, 2021 Sentence-level extractive summarization is a fundamental yet challenging task, and recent powerful approaches prefer to pick sentences sorted by the predicted probabilities until the length limit is reached, a.k.a. ``Top-K Strategy''.
Ruipeng Jia +5 more
semanticscholar +1 more source
Language independent extractive summarization [PDF]
Proceedings of the ACL 2005 on Interactive poster and demonstration sessions - ACL '05, 2005 We demonstrate TextRank -- a system for unsupervised extractive summarization that relies on the application of iterative graph-based ranking algorithms to graphs encoding the cohesive structure of a text. An important characteristic of the system is that it does not rely on any language-specific knowledge resources or any manually constructed training
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IEEE AccessBiomedical multi-document summarization (BioMDSum) involves automatically generating concise and informative summaries from collections of related biomedical documents.
Azzedine Aftiss +3 more
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Neural Latent Extractive Document Summarization [PDF]
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018 Extractive summarization models require sentence-level labels, which are usually created heuristically (e.g., with rule-based methods) given that most summarization datasets only have document-summary pairs. Since these labels might be suboptimal, we propose a latent variable extractive model where sentences are viewed as latent variables and sentences
Zhang, Xingxing +3 more
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Deep Differential Amplifier for Extractive Summarization
Annual Meeting of the Association for Computational Linguistics, 2021 For sentence-level extractive summarization, there is a disproportionate ratio of selected and unselected sentences, leading to flatting the summary features when maximizing the accuracy.
Ruipeng Jia +6 more
semanticscholar +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Surveillance imaging aims to detect tumour relapse before symptoms develop, but it's unclear whether earlier detection of relapse leads to better outcomes in children and young people (CYP) with medulloblastoma and ependymoma. This systematic review aims to identify relevant literature to determine the efficacy of surveillance magnetic ...
Lucy Shepherd +3 more
wiley +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Bone tumours present significant challenges for affected patients, as multimodal therapy often leads to prolonged physical limitations. This is particularly critical during childhood and adolescence, as it can negatively impact physiological development and psychosocial resilience.
Jennifer Queisser +5 more
wiley +1 more source