Automatic text processing
Computer Evaluation of Indexing and Text Processing
Journal of the ACM (JACM)
The Journal of Machine Learning Research
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
A survey for multi-document summarization
HLT-NAACL-DUC '03 Proceedings of the HLT-NAACL 03 on Text summarization workshop - Volume 5
Extraction of relevant figures and tables for multi-document summarization
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
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This paper reports an initial study that aims to assess the viability of a state-of-the-art multi-document summarizer for automatic captioning of geo-referenced images. The automatic captioning procedure requires summarizing multiple web documents that contain information related to images' location. We use SUMMA (Saggion and Gaizauskas, 2005) to generate generic and query-based multi-document summaries and evaluate them using ROUGE evaluation metrics (Lin, 2004) relative to human generated summaries. Results show that, even though query-based summaries perform better than generic ones, they are still not selecting the information that human participants do. In particular, the areas of interest that human summaries display (history, travel information, etc.) are not contained in the query-based summaries. For our future work in automatic image captioning this result suggests that developing the query-based summarizer further and biasing it to account for user-specific requirements will prove worthwhile.