Publication2020
Abstractive text summarization and unsupervised text classifier
The record
- Type
- Publication
- Date
- 1 January 2020
- Level
- National
- Journal
- Advances in Intelligent Systems and Computing
- Volume
- 1101
- Pages
- 355-365
- Published
- 1 January 2020
- DOI
- 10.1007/978-981-15-1884-3_33
- Scopus ID
- 85083666279
- ISSN
- 21945357
- Authors
- 3
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Sequence-to-sequence RNN and bidirectional LSTM implementation for text processing demonstrates machine learning algorithm development without named application domain.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
Research advances artificial intelligence capabilities in natural language processing and text understanding through neural network architectures.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “Abstractive text summarization and unsupervised text classifier”. Kortex.