4 documents found, page 1 of 1
Event-based summarization using a centrality-as-relevance model
Marujo, L.; Ribeiro, R.; Gershman, A.; de Matos, D. M.; Neto, J. P.; Carbonell, J.Event detection is a fundamental information extraction task, which has been explored largely in the context of question answering, topic detection and tracking, knowledge base population, news recommendation, and automatic summarization. In this article, we explore an event detection framework to improve a key phrase-guided centrality-based summarization model. Event detection is based on the fuzzy fingerprint...
Using generic summarization to improve music information retrieval tasks
Raposo, F.; Ribeiro, R.; de Matos, D. M.In order to satisfy processing time constraints, many music information retrieval (MIR) tasks process only a segment of the whole music signal. This may lead to decreasing performance, as the most important information for the tasks may not be in the processed segments. We leverage generic summarization algorithms, previously applied to text and speech, to summarize items in music datasets. These algorithms bui...
On the application of generic summarization algorithms to music
Raposo, F.; Ribeiro, R.; de Matos, D. M.Several generic summarization algorithms were developed in the past and successfully applied in fields such as text and speech summarization. In this paper, we review and apply these algorithms to music. To evaluate their performance, we adopt an extrinsic approach: we compare a Fado genre classifier's performance using truncated contiguous clips against the summaries extracted with those algorithms on two diff...
Revisiting Centrality-as-Relevance: Support Sets and Similarity as Geometric Pr...
Ribeiro, R.; de Matos, D. M.In automatic summarization, centrality-as-relevance means that the most important content of an information source, or a collection of information sources, corresponds to the most central passages, considering a representation where such notion makes sense (graph, spatial, etc.). We assess the main paradigms, and introduce a new centrality-based relevance model for automatic summarization that relies on the use...