Summarization of Historical Articles Using Temporal Event Clustering
نویسندگان
چکیده
In this paper, we investigate the use of temporal information for improving extractive summarization of historical articles. Our method clusters sentences based on their timestamps and temporal similarity. Each resulting cluster is assigned an importance score which can then be used as a weight in traditional sentence ranking techniques. Temporal importance weighting offers consistent improvements over baseline systems.
منابع مشابه
Ha, Eun Young. Modeling Discourse Structure and Temporal Event Relations for Automated Document Summarization with Markov Logic Networks. (under the Direction of Modeling Discourse Structure and Temporal Event Relations for Automated Document Summarization with Markov Logic Networks
HA, EUN YOUNG. Modeling Discourse Structure and Temporal Event Relations for Automated Document Summarization with Markov Logic Networks. (Under the direction of James C. Lester.) Recent years have seen significant progress in natural language processing. A key challenge posed by many natural language applications ranging from text summarization to question answering and machine translation is ...
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