Publications
2023
1.
Joshi, Akanksha; Fidalgo, Eduardo; Alegre, Enrique; Fernández-Robles, Laura
DeepSumm: Exploiting topic models and sequence to sequence networks for extractive text summarization Artículo de revista
En: Expert Systems with Applications, vol. 211, pp. 118442, 2023, (Publisher: Pergamon).
Resumen | Enlaces | BibTeX | Etiquetas: deep learning, Extractive Summarization, Topic Modeling, Word embedding
@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}
}
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.
2022
2.
Joshi, Akanksha; Fidalgo, Eduardo; Alegre, Enrique; Alaiz-Rodriguez, Rocio
RankSum—An unsupervised extractive text summarization based on rank fusion Artículo de revista
En: Expert Systems with Applications, vol. 200, pp. 116846, 2022, (Publisher: Pergamon).
Resumen | Enlaces | BibTeX | Etiquetas: Extractive Summarization, Sentence Ranking, Text summarization, Unsupervised Learning
@article{joshi_ranksumunsupervised_2022,
title = {RankSum—An unsupervised extractive text summarization based on rank fusion},
author = {Akanksha Joshi and Eduardo Fidalgo and Enrique Alegre and Rocio Alaiz-Rodriguez},
url = {https://www.sciencedirect.com/science/article/pii/S0957417422002998},
year = {2022},
date = {2022-01-01},
journal = {Expert Systems with Applications},
volume = {200},
pages = {116846},
abstract = {This paper introduces Ranksum, an approach for extractive text summarization that combines four multi-dimensional sentence features: topic information, semantic content, significant keywords, and position. It ranks sentences based on these features using an unsupervised method, followed by a weighted fusion to determine sentence significance. The method utilizes probabilistic topic models for topic ranking, sentence embeddings for semantic information, and graph-based strategies for identifying keywords. The approach also employs a novelty measure to avoid redundancy. Experimental results on datasets like CNN/DailyMail and DUC 2002 show that Ranksum outperforms existing summarization methods.},
note = {Publisher: Pergamon},
keywords = {Extractive Summarization, Sentence Ranking, Text summarization, Unsupervised Learning},
pubstate = {published},
tppubtype = {article}
}
This paper introduces Ranksum, an approach for extractive text summarization that combines four multi-dimensional sentence features: topic information, semantic content, significant keywords, and position. It ranks sentences based on these features using an unsupervised method, followed by a weighted fusion to determine sentence significance. The method utilizes probabilistic topic models for topic ranking, sentence embeddings for semantic information, and graph-based strategies for identifying keywords. The approach also employs a novelty measure to avoid redundancy. Experimental results on datasets like CNN/DailyMail and DUC 2002 show that Ranksum outperforms existing summarization methods.
2019
3.
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.