Automatic Summarization of Stock Market News Articles

Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval Pub Date : 2020-12-18 DOI:10.1145/3443279.3443289

J. Logeesan, Y. Rishoban, H. A. Caldera

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Abstract

Stock market news articles published by leading companies are read by every trader to carry out their trading activities as they provide real time and reliable information about the organization. These news articles help in analyzing and identifying essential facts in trading. If these facts can be quickly captured from the articles could lead to look for more articles for better accuracy on their decision making. This research focuses on the single document based abstractive summarization of stock market investment news articles for traders. A summarization tool to extract the salient sentences from stock market investment news article on trading is developed in this research. In methodology, A keyword based weighting in extracting the sentences are used to enrich the domain relevancy. Domain is one of the deterministic factors in summarization which helps to correctly interpret the words. Finally an efficient graph algorithm is used to obtain the fluent summary. Then these summaries were compared with the domain expert summary to identify how far the summarization is useful for the traders.

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股票市场新闻文章的自动摘要

每个交易者都会阅读由领先公司发布的股票市场新闻，以进行他们的交易活动，因为它们提供了有关该组织的实时和可靠的信息。这些新闻文章有助于分析和识别交易中的基本事实。如果这些事实可以从文章中快速捕获，可能会导致寻找更多的文章，以提高他们决策的准确性。本文主要研究基于单文档的股票市场投资新闻文章的抽象摘要。本研究开发了一种从股票市场投资新闻交易文章中提取重要句子的摘要工具。在方法上，采用基于关键字的加权方法提取句子，以丰富领域相关性。领域是摘要的决定性因素之一，它有助于正确地解释词语。最后，采用一种高效的图算法来获得流畅的摘要。然后将这些总结与领域专家总结进行比较，以确定总结对交易者的有用程度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval

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