对新闻播报节目进行自动主题划分,可以有效地组织和利用新闻播报类数据。目前自动故事单元划分的研究以视频数据为主,音频的语音识别文本中包含丰富的语义信息,同时声音事件的转换也可以提供很多重要信息,能够有效的进行基于语义的主题划分。根据这些信息,该文提出了一种基于规则的多信息融合的方法,利用切分点邻域的音频类型信息来修正使用语义信息的切分结果,完成主题划分。实验表明根据规则进行特征融合后,新闻节目主题划分的F-估值为64.8%,错误概率Pk和WindowDiff分别达到18.3%和24.5%。
Abstract
Automatic story segmentation is very important for retrieval of broadcast news data. Recent research on automatic story segmentation is focused on video data. Semantic information extracted from speech recognition results and acoustic event information of audio data provide important information for story segmentation. This paper proposes a rule-based multi-information fusion method, using the audio information to adjust the results of text story segmentation. Experiments show that after fusing multi-information, the F-measure of automatic story segmentation of broadcast news data reaches 64.8%, Pk and WindowDiff reach 18.3% and 24.5% respectively.
Key wordsautomatic story segmentation; improved SeLeCT algorithm; multi-information fusion
关键词
新闻节目主题划分 /
改进的SeLeCT算法 /
信息融合
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Key words
automatic story segmentation /
improved SeLeCT algorithm /
multi-information fusion
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参考文献
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