# An Unsupervised Semantic Sentence Ranking Scheme for Text Documents

arXiv: https://arxiv.org/abs/2005.02158 · HTML: https://mlsanity.com/paper/2005.02158/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Contextual Networks and Unsupervised Ranking of Sentences](https://mlsanity.com/paper/2203.04459/) (2022)
- [Content Significance Distribution of Sub-Text Blocks in Articles and Its Application to Article-Organization Assessment](https://mlsanity.com/paper/2311.01673/) (2023)
- [Is Crowdsourcing Breaking Your Bank? Cost-Effective Fine-Tuning of Pre-trained Language Models with Proximal Policy Optimization](https://mlsanity.com/paper/2402.18284/) (2024)
- [Sequencing Matters: A Generate-Retrieve-Generate Model for Building Conversational Agents](https://mlsanity.com/paper/2311.09513/) (2023)

## Abstract (arXiv)

This paper presents Semantic SentenceRank (SSR), an unsupervised scheme for automatically ranking sentences in a single document according to their relative importance. In particular, SSR extracts essential words and phrases from a text document, and uses semantic measures to construct, respectively, a semantic phrase graph over phrases and words, and a semantic sentence graph over sentences. It applies two variants of article-structure-biased PageRank to score phrases and words on the first graph and sentences on the second graph. It then combines these scores to generate the final score for each sentence. Finally, SSR solves a multi-objective optimization problem for ranking sentences based on their final scores and topic diversity through semantic subtopic clustering. An implementation of SSR that runs in quadratic time is presented, and it outperforms, on the SummBank benchmarks, each individual judge's ranking and compares favorably with the combined ranking of all judges.