Zobrazeno 1 - 10
of 760
pro vyhledávání: '"Abstractive summarization"'
Autor:
Svetlana G. Sorokina
Publikováno v:
Виртуальная коммуникация и социальные сети, Vol 3, Iss 3, Pp 203-222 (2024)
Interest in innovative technological strategies and modern digital tools has increased significantly due to the need to manage large amounts of unstructured data. This paper reviews current paradigms and services for automated summarization, develope
Externí odkaz:
https://doaj.org/article/2a63f0b226ae4ad6899cb6f1e42c2567
Publikováno v:
PeerJ Computer Science, Vol 10, p e2424 (2024)
With the exponential proliferation of digital documents, there arises a pressing need for automated document summarization (ADS). Summarization, a compression technique, condenses a source document into concise sentences that encapsulate its salient
Externí odkaz:
https://doaj.org/article/76fd9b0bf9b24eeaa1b757f5bce69a23
Publikováno v:
IEEE Access, Vol 12, Pp 139302-139315 (2024)
Abstractive summarization models are required to generate summaries that maintain factual consistency with the source text and exhibit high diversity to be applicable in practical applications. Existing models, which are based on pre-trained sequence
Externí odkaz:
https://doaj.org/article/314e0bce07a54572bf9cc36155c9924c
Publikováno v:
Journal of Social Computing, Vol 5, Iss 2, Pp 132-144 (2024)
Social media’s explosive growth has resulted in a massive influx of electronic documents influencing various facets of daily life. However, the enormous and complex nature of this content makes extracting valuable insights challenging. Long documen
Externí odkaz:
https://doaj.org/article/abb75a896ac64903b4c950ac38c0b6ef
Autor:
Nikita Glazkov, Ilya Makarov
Publikováno v:
IEEE Access, Vol 12, Pp 150793-150806 (2024)
The field of dialogue summarization has advanced significantly with large language models (LLMs), but their effectiveness can be limited by the size and diversity of training data, as well as concerns about bias. This study proposes a data augmentati
Externí odkaz:
https://doaj.org/article/6e1959b1c8d64b1db3c118cc6bd558d8
Autor:
Pratik K. Biswas
Publikováno v:
IEEE Access, Vol 12, Pp 146620-146634 (2024)
Text summarization is the process of condensing a piece of text to fewer sentences, while still preserving its content. Chat transcript, in this context, is a textual copy of a digital or online conversation between a customer (caller) and agent(s).
Externí odkaz:
https://doaj.org/article/8dfdb075d82942239b2f6cfa04c50dc8
Publikováno v:
IEEE Access, Vol 12, Pp 119174-119184 (2024)
The paper introduces VATMAN (Video-Audio-Text Multimodal Abstractive summarizatioN), a novel approach for generating hierarchical multimodal summaries utilizing Trimodal Hierarchical Multi-head Attention. Unlike existing generative pre-trained langua
Externí odkaz:
https://doaj.org/article/056f3c04e0d44a938767f7d812ea1949
Autor:
Daniil Chernyshev, Boris Dobrov
Publikováno v:
IEEE Access, Vol 12, Pp 47219-47230 (2024)
Recent advances in low-resource abstractive summarization were largely made through the adoption of specialized pre-training, pseudo-summarization, that integrates the content selection knowledge through various centrality-based sentence recovery tas
Externí odkaz:
https://doaj.org/article/b870b80545c042528dd2d77cb21fc0f7
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-34 (2023)
Abstract Due to the exponential growth of online information, the ability to efficiently extract the most informative content and target specific information without extensive reading is becoming increasingly valuable to readers. In this paper, we pr
Externí odkaz:
https://doaj.org/article/6005c6aabda94151b11bfb6db0623fa4
Autor:
Philip Ehnert, Julian Schröter
Publikováno v:
Frontiers in Artificial Intelligence, Vol 7 (2024)
Identifying key statements in large volumes of short, user-generated texts is essential for decision-makers to quickly grasp their key content. To address this need, this research introduces a novel abstractive key point generation (KPG) approach app
Externí odkaz:
https://doaj.org/article/eb41934254574d73a2d94b098be269f9