FastLexRank: Efficient Lexical Ranking for Structuring Social Media Posts
Mao Li, Frederick Conrad, Johann Gagnon-Bartsch

TL;DR
FastLexRank is a scalable, efficient implementation of LexRank that reduces computational complexity from quadratic to linear, enabling real-time processing of large social media datasets without sacrificing accuracy.
Contribution
The paper introduces algorithmic improvements that significantly enhance LexRank's efficiency, making it suitable for large-scale, real-time social media text analysis.
Findings
Achieves linear time complexity for LexRank computations
Maintains identical ranking results as the original LexRank
Demonstrates effectiveness on social media datasets
Abstract
We present FastLexRank\footnote{https://github.com/LiMaoUM/FastLexRank}, an efficient and scalable implementation of the LexRank algorithm for text ranking. Designed to address the computational and memory complexities of the original LexRank method, FastLexRank significantly reduces time and memory requirements from to without compromising the quality or accuracy of the results. By employing an optimized approach to calculating the stationary distribution of sentence graphs, FastLexRank maintains an identical results with the original LexRank scores while enhancing computational efficiency. This paper details the algorithmic improvements that enable the processing of large datasets, such as social media corpora, in real-time. Empirical results demonstrate its effectiveness, and we propose its use in identifying central tweets, which can be further…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Advanced Text Analysis Techniques · Web Data Mining and Analysis
