Variations of the Similarity Function of TextRank for Automated Summarization

By Federico Barrios, Federico López, Luis Argerich, Rosa Wachenchauzer. 2016

262 citations recorded by the source corpus.

This article presents new alternatives to the similarity function for the TextRank algorithm for automatic summarization of texts. We describe the generalities of the algorithm and the different functions we propose. Some of these variants achieve a significative improvement using the same metrics and dataset as the original publication.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

MLSUM: The Multilingual Summarization Corpus (2020, methodology): “We used the implementation provided by Barrios et al. (2016b).”
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies (2018, methodology): “The sys-tem was originally developed by Mihalcea and Ta-rau (2004) and was later further developed and improved by Barrios et al. (2016).”
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization (2019, methodology): “For T EXT R ANK , we used the summanlp 7 (Barrios et al., 2016) to generate summary with three sentences based on T EX -”
WikiHow: A Large Scale Text Summarization Dataset (2018, methodology): “TextRank Extractive system: An extractive summarization system (Mihalcea and Tarau, 2004; Barrios et al., 2016) using a graph-based ranking method to select sentences from the article and form the summary.”
How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature (2019, methodology): “We use the Gensim.summarization package (Barrios et al., 2016) to produce these summaries.”
Legal Information Retrieval with Generalized Language Models (2019, unknown): “For this reason, we introduce a summarization of the texts in the corpus, as implemented in gensim[13] using TextRank[1].”
AlignScore: Evaluating Factual Consistency with A Unified Alignment Function (2023, methodology): “For the WikiHow dataset, we use source documents as text a, and the ground truth summaries together with extractive summaries generated by an extractive summarizer (Barrios et al., 2016) as text b to form ALIGNED samples.”
The Summary Loop: Learning to Write Abstractive Summaries Without Examples (2020, background): “Most unsupervised summarization work is extractive: sentences deemed relevant are pulled out of the original document and stitched into a summary, based on a heuristic for a sentence’s relevance (Mihalcea and Tarau, 2004; Barrios et al., 2015; West et al., 2019).”