Publications

@article{joshi_deepsumm_2023,

title = {DeepSumm: Exploiting topic models and sequence to sequence networks for extractive text summarization},

author = {Akanksha Joshi and Eduardo Fidalgo and Enrique Alegre and Laura Fernández-Robles},

url = {https://www.sciencedirect.com/science/article/pii/S0957417422015391},

year  = {2023},

date = {2023-01-01},

journal = {Expert Systems with Applications},

volume = {211},

pages = {118442},

abstract = {This paper introduces DeepSumm, a method for extractive text summarization that combines topic modeling and word embeddings to improve summary quality. DeepSumm uses topic vectors and sequence networks to capture both local and global semantics in a document. It calculates scores for each sentence using Sentence Topic Score (STS), Sentence Content Score (SCS), Sentence Novelty Score (SNS), and Sentence Position Score (SPS), and combines them into a Final Sentence Score (FSS). The method outperforms existing approaches on the DUC 2002 and CNN/DailyMail datasets with improved ROUGE scores.},

note = {Publisher: Pergamon},

keywords = {deep learning, Extractive Summarization, Topic Modeling, Word embedding},

pubstate = {published},

tppubtype = {article}

}