Results 121 to 130 of about 7,619 (156)

ClinicSum: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations. [PDF]

Proc IEEE Int Conf Big Data
Neupane S, Tripathi H, Mitra S, Bozorgzad S, Mittal S, Rahimi S, Amirlatifi A.   +6 more
europepmc   +1 more source

Multimodal Abstractive Summarization using bidirectional encoder representations from transformers with attention mechanism. [PDF]

Heliyon
Argade D, Khairnar V, Vora D, Patil S, Kotecha K, Alfarhood S.   +5 more
europepmc   +1 more source

Key point generation as an instrument for generating core statements of a political debate on Twitter. [PDF]

Front Artif Intell
Ehnert P, Schröter J.
europepmc   +1 more source

Ascle-A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study.

J Med Internet Res
Yang R, Zeng Q, You K, Qiao Y, Huang L, Hsieh CC, Rosand B, Goldwasser J, Dave A, Keenan T, Ke Y, Hong C, Liu N, Chew E, Radev D, Lu Z, Xu H, Chen Q, Li I.   +18 more
europepmc   +1 more source

Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: Benchmark Study.

JMIR Ment Health
Adhikary PK, Srivastava A, Kumar S, Singh SM, Manuja P, Gopinath JK, Krishnan V, Gupta SK, Deb KS, Chakraborty T.   +9 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Extractive Summarization on Food Reviews

The Journal of The Institution of Engineers, Malaysia, 2022
Text summarization is a technique to summarize the content of a sizeable text but meanwhile it keep the key information. Extractive summarization and abstractive summarization are the main techniques for text summarization. TextRank algorithm, an extractive summarization technique is applied to perform automatic text summarization in this study ...
Yuen Kei Khor, Chi Wee Tan, Tong Ming Lim   +2 more
openaire   +1 more source

Memory-Based Extractive Summarization

2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2018
Recurrent neural networks (RNNs) have been widely used in previous work on extractive text summarization. However, the information stored by RNNs structure is typically limited and is not compartmentalized enough to accurately record facts from the past.
Chong Feng, Zhiqiang Pan, Jianming Zheng, Ying Xu   +3 more
openaire   +1 more source

Event-Based Extractive Summarization

2004
Most approaches to extractive summarization define a set of features upon which selection of sentences is based, using algorithms independent of the features themselves. We propose a new set of features based on low-level, atomic events that describe relationships between important actors in a document or set of documents.
Filatova, Elena, Hatzivassiloglou, Vasileios   +1 more
openaire   +1 more source