TextRank: Bringing Order into Text

By Rada Mihalcea, Paul Tarau. 2004

4,299 citations recorded by the source corpus.

Subfields: Information Retrieval & Search, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Retrieval & Search

In Natural Language Processing

Citations in the corpus

Generating Wikipedia by Summarizing Long Sequences (2018, background): “TextRank (Mihalcea & Tarau, 2004): A weighted graph is deﬁned where text units are nodes and edges are deﬁned by a similarity measure based on word overlap.”
MLSUM: The Multilingual Summarization Corpus (2020, methodology): “…of training data available, DUC datasets are often used in a domain adaptation setup for models ﬁrst trained on larger datasets such as Gigaword, CNN/DM (Nallapati et al., 2016; See et al., 2017) or with unsupervised methods (Dorr et al., 2003; Mihalcea and Tarau, 2004; Barrios et al., 2016a).”
Recursively Summarizing Books with Human Feedback (2021, methodology): “…(Mihalcea and Ceylan, 2007; Ladhak et al., 2020; Kry´sci´nski et al., 2021) and existing methods are typically either extractive (Radev et al., 2004; Mihalcea and Tarau, 2004; Bamman and Smith, 2013; Ladhak et al., 2020) or focused on shorter stories (Kazantseva, 2006; Zhang et al., 2019b).”
Regularizing Text Categorization with Clusters of Words (2016, methodology): “Graph-of-words was originally an idea of Mihalcea and Tarau (2004) and Erkan and Radev (2004) who applied it to the tasks of unsupervised keyword extraction and extractive single document summarization.”
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization (2004, methodology): “An eigenvector centrality algorithm on weighted graphs was independently proposed by Mihalcea and Tarau (2004) for single-document summarization.”
Ranking Sentences for Extractive Summarization with Reinforcement Learning (2018, background): “…majority of these methods learn to score each sentence independently (Barzilay and Elhadad, 1997; Teufel and Moens, 1997; Erkan and Radev, 2004; Mihalcea and Tarau, 2004; Shen et al., 2007; Schilder and Kondadadi, 2008; Wan, 2010) and a summary is generated by selecting top-scored sentences in…”
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization (2019, methodology): “For L EX R ANK and S UM B ASIC , we used sumy 8 .”
This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation (2019, background): “For TextRank and LexRank , we use the sumy 2 implementation which uses the snowball stemmer, the sentence and word tok-enizer from NLTK 3 .”
Neural Document Summarization by Jointly Learning to Score and Select Sentences (2018, methodology): “T EXT R ANK An unsupervised algorithm based on weighted-graphs proposed by Mihalcea and Tarau (2004).”
BanditSum: Extractive Summarization as a Contextual Bandit (2018, methodology): “Recently, neural network-based meth-ods have been gaining popularity over classical methods (Luhn, 1958; Gong and Liu, 2001; Con-roy and O’leary, 2001; Mihalcea and Tarau, 2004; Wong et al., 2008), as they have demonstrated stronger performance on large corpora.”
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models (2019, methodology): “We chose Text-Rank (Mihalcea and Tarau, 2004) – an extractive non-parametric summarization system inspired by PageRank (Page et al., 1999) – since it is well performing for extractive tasks and could be easily adapted for our needs.”
Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval (2019, methodology): “A rich research literature studies how to better estimate term importance (e.g., [2, 3, 5, 18, 27, 36]), however frequency-based signals continue to dominate in both industry and academia.”
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models (2020, methodology): “They rank the importance of paragraphs found in the reference material based on techniques such as TextRank (Mihalcea and Tarau, 2004), a graph based ranking technique.”
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.”
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation (2020, methodology): “In another line of work, extractive models such as TextRank, (Mihal-cea and Tarau, 2004), LexRank (Erkan and Radev, 2004), and more recently PacSum (Zheng and Lap-ata, 2019), aim to use graph centrality in order to extract important sentences for a document.”
Abstractive Document Summarization with a Graph-Based Attentional Neural Model (2017, methodology): “To tackle this challenge, we learn from graph-based extractive summarization models TextRank (Mihalcea and Tarau, 2004) and LexRank (Erkan and Radev, 2004), which are based on the PageR-ank (Page et al., 1999) algorithm.”
CTRLsum: Towards Generic Controllable Text Summarization (2020, background): “Compared to typical keywords extraction methods (Riloff and Lehnert, 1994; Mihalcea and Tarau, 2004) which output only a few salient words, our extraction retains most content words found in the summary.”