关键词: 新闻脉络, 词覆盖, 可解释, 健壮主成分分析, 随机游走

Abstract:

Current studies merely focus on a story chain’s similarity of topic relationship and importance of documents, whilst almost ignoring its logical coherency and explainability. Along with algorithm complexity brought about by exponential growth in sets of news data, a story chain from word coverage perspective is constructed, taking advantage of the story comments to position the turning point of each event. The ideas of similarity of topic relationship and sparsity differences as well as RPCA approach are used to conduct logical modeling for the documents. Random walk and graph traversals are adopted to quantify and construct an explainable and logically coherent story chain. The double-blind experiment reveals that proposed method outperforms other algorithms.

Key words: story chain, word coverage, explainable, RPCA, random walk