Publications

@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 Rocío Alaiz-Rodríguez},

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}

}

@article{dominguez_application_2019,

title = {Application of extractive text summarization algorithms to speech-to-text media},

author = {Víctor Domínguez and Eduardo Fidalgo and Rubel Biswas and Enrique Alegre and Laura Fernández-Robles},

url = {https://link.springer.com/chapter/10.1007/978-3-030-29859-3_46},

year  = {2019},

date = {2019-01-01},

journal = {Hybrid Artificial Intelligent Systems: 14th International Conference, HAIS 2019, León, Spain, September 4–6, 2019, Proceedings 14},

pages = {540–550},

abstract = {This paper evaluates six extractive text summarization algorithms for speech-to-text summarization. The study assesses Luhn, TextRank, LexRank, LSA, SumBasic, and KLSum using ROUGE metrics on two datasets (DUC2001 and OWIDSum). Additionally, five speech documents from the ISCI Corpus were transcribed using Google Cloud Speech API and summarized. Results indicate that Luhn and TextRank perform best for extractive speech-to-text summarization.},

note = {Publisher: Springer International Publishing},

keywords = {AI, machine learning, natural languaje processing, speech to text, Text summarization},

pubstate = {published},

tppubtype = {article}

}