Publications
2019
1.
Joshi, Akanksha; Fidalgo, Eduardo; Alegre, Enrique; Fernández-Robles, Laura
SummCoder: An unsupervised framework for extractive text summarization based on deep auto-encoders Artículo de revista
En: Expert Systems with Applications, vol. 129, pp. 200–215, 2019, (Publisher: Pergamon).
Resumen | Enlaces | BibTeX | Etiquetas: Auto-Encoder, deep learning, Extractive Summarization, Extractive Text Summarization, Sentence Embedding, Tor Darknet
@article{joshi_summcoder_2019,
title = {SummCoder: An unsupervised framework for extractive text summarization based on deep auto-encoders},
author = {Akanksha Joshi and Eduardo Fidalgo and Enrique Alegre and Laura Fernández-Robles},
url = {https://www.sciencedirect.com/science/article/pii/S0957417419302192},
year = {2019},
date = {2019-01-01},
journal = {Expert Systems with Applications},
volume = {129},
pages = {200–215},
abstract = {This paper introduces SummCoder, a method for extractive text summarization using three metrics: content relevance, novelty, and position relevance. The model performs well on datasets like DUC 2002, Blog Summarization, and a new dataset, TIDSumm, focused on web documents from the Tor network. SummCoder outperforms or matches state-of-the-art methods based on ROUGE metrics, providing useful applications for Law Enforcement Agencies.},
note = {Publisher: Pergamon},
keywords = {Auto-Encoder, deep learning, Extractive Summarization, Extractive Text Summarization, Sentence Embedding, Tor Darknet},
pubstate = {published},
tppubtype = {article}
}
This paper introduces SummCoder, a method for extractive text summarization using three metrics: content relevance, novelty, and position relevance. The model performs well on datasets like DUC 2002, Blog Summarization, and a new dataset, TIDSumm, focused on web documents from the Tor network. SummCoder outperforms or matches state-of-the-art methods based on ROUGE metrics, providing useful applications for Law Enforcement Agencies.