快速计算社交媒体文本中心性，效率提升至线性复杂度。
FastLexRank: Efficient Lexical Ranking for Structuring Social Media Posts
- 用优化算法将句子图的稳态分布计算从O(n²)降至O(n)
- 处理大规模社交文本数据时保持原结果精度，支持实时分析
- 适合需要高效识别关键推文的舆情分析与内容摘要场景
我们提出FastLexRank，一种高效可扩展的文本排序算法实现，针对原始LexRank方法在计算和内存上的高复杂度问题。FastLexRank将时间与内存开销从O(n²)显著降低至O(n)，同时不牺牲结果的质量或准确性。通过优化句子图的稳态分布计算方式，该方法在保持与原LexRank得分一致的前提下大幅提升计算效率。本文详细阐述了算法改进机制，使其能够实现实时处理大规模社交媒体语料库。实验结果证明其有效性，我们建议将其用于识别核心推文，后续可结合高级NLP技术进行深入分析。FastLexRank为文本中心性计算提供了一种可扩展的解决方案，满足日益增长的数字内容高效处理需求。
原文摘要 · Abstract (English)
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 $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$ 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 analyzed using advanced NLP techniques. FastLexRank offers a scalable solution for text centrality calculation, addressing the growing need for efficient processing of digital content.
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