# Biased TextRank: Unsupervised Graph-Based Content Extraction

arXiv: https://arxiv.org/abs/2011.01026 · HTML: https://mlsanity.com/paper/2011.01026/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 14 of 14)

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- [Query-Focused Extractive Summarization for Sentiment Explanation](https://mlsanity.com/paper/2509.11989/) (2025)

## Abstract (arXiv)

We introduce Biased TextRank, a graph-based content extraction method inspired by the popular TextRank algorithm that ranks text spans according to their importance for language processing tasks and according to their relevance to an input "focus." Biased TextRank enables focused content extraction for text by modifying the random restarts in the execution of TextRank. The random restart probabilities are assigned based on the relevance of the graph nodes to the focus of the task. We present two applications of Biased TextRank: focused summarization and explanation extraction, and show that our algorithm leads to improved performance on two different datasets by significant ROUGE-N score margins. Much like its predecessor, Biased TextRank is unsupervised, easy to implement and orders of magnitude faster and lighter than current state-of-the-art Natural Language Processing methods for similar tasks.