Multi-document summarization for Turkish news

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) Pub Date : 2017-09-01 DOI:10.1109/IDAP.2017.8090189

Ferhat Demirci, E. Karabudak, B. Ilgen

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引用次数: 1

Abstract

In this paper, we introduce our multi-document summarization system for Turkish news. The aim of the summarization system is to build a single document for multi document news that have been collected previously. The news were collected from several Turkish news sources via Real Simple Syndication (RSS). They were separated into clusters according to their topics. We utilized cosine similarity metric for the clustering process. Latent Semantic Analysis (LSA) has been used in the summarization phase. Multi-Document Summarization (MDS) differs from single document summarization in that the issues of compression, speed, redundancy and passage selection are essential inside the formation of ideal summaries. In this study, we utilized term frequency in document scoring which let us select the sentences with higher importance degree. We use ROUGE technique for evaluation of the system and our results show that the average of recall and precision percentage of this system is 43%. In the manual summarization phase, fifteen volunteers took part. The reason of low percentage is interpreted as getting texts randomly without any edit. It has been observed that the number of sentences and rate of summarization affect the accuracy rate.

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土耳其新闻的多文档摘要

本文介绍了我们的土耳其新闻多文献摘要系统。摘要系统的目的是将以前收集到的多文档新闻构建成一个单一的文档。这些新闻是通过RSS从几个土耳其新闻来源收集的。他们根据主题被分成几组。我们使用余弦相似度度量进行聚类过程。在摘要阶段使用了潜在语义分析(LSA)。多文档摘要(Multi-Document Summarization, MDS)与单文档摘要的不同之处在于，在理想摘要的形成过程中，压缩、速度、冗余和段落选择等问题至关重要。在本研究中，我们利用词频对文档进行评分，使我们能够选择重要性较高的句子。采用ROUGE技术对该系统进行评价，结果表明，该系统的平均查全率和查准率为43%。在手工总结阶段，有15名志愿者参加。低百分比的原因被解释为随机获取文本而没有任何编辑。研究发现，句子的数量和总结的速度会影响翻译的正确率。

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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2017 International Artificial Intelligence and Data Processing Symposium (IDAP)

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