Results 31 to 40 of about 3,357,090 (344)
Multi-Dimensional Evaluation of Text Summarization with In-Context Learning [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Evaluation of natural language generation (NLG) is complex and multi-dimensional. Generated text can be evaluated for fluency, coherence, factuality, or any other dimensions of interest.
Sameer Jain +6 more
semanticscholar +1 more source
Automatic Text Summarization Methods: A Comprehensive Review [PDF]
arXiv.org, 2022 One of the most pressing issues that have arisen due to the rapid growth of the Internet is known as information overloading. Simplifying the relevant information in the form of a summary will assist many people because the material on any topic is ...
Divakar Yadav, Jalpa J Desai, A. Yadav
semanticscholar +2 more sources
Graph-Based Extractive Text Summarization Models: A Systematic Review [PDF]
Journal of Information Technology Management, 2022 The volume of digital text data is continuously increasing both online and offline storage, which makes it difficult to read across documents on a particular topic and find the desired information within a possible available time.
Abdulkadir Bichi +3 more
doaj +1 more source
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond [PDF]
Conference on Computational Natural Language Learning, 2016 In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora.
Ramesh Nallapati +4 more
semanticscholar +1 more source
ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 ChatGPT ...
Dongqi Liu, Vera Demberg
semanticscholar +1 more source
Get To The Point: Summarization with Pointer-Generator Networks [PDF]
Annual Meeting of the Association for Computational Linguistics, 2017 Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text).
A. See +2 more
semanticscholar +1 more source
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization [PDF]
Journal of Artificial Intelligence Research, 2004 We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS).
Günes Erkan, Dragomir R. Radev
semanticscholar +1 more source
Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts
Research Square, 2023 Sifting through vast textual data and summarizing key information from electronic health records (EHR) imposes a substantial burden on how clinicians allocate their time.
Dave Van Veen +14 more
semanticscholar +1 more source
International Journal of Engineering and Computer Science, 2020 In this new era, where tremendous information is available on the internet, it is most important to provide the improved mechanism to extract the information quickly and most efficiently. It is very difficult for human beings to manually extract the summary of a large documents of text. There are plenty of text material available on the internet.
A. Vikas +2 more
openaire +3 more sources
Multilingual Text Summarization for German Texts Using Transformer Models
Information, 2023 The tremendous increase in documents available on the Web has turned finding the relevant pieces of information into a challenging, tedious, and time-consuming activity.
Tomas Humberto Montiel Alcantara +3 more
doaj +1 more source