30 research outputs found
On the Application of Generic Summarization Algorithms to Music
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 this
summarization's 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 2 different datasets. We show that
Maximal Marginal Relevance (MMR), LexRank and Latent Semantic Analysis (LSA)
all improve classification performance in both datasets used for testing.Comment: 12 pages, 1 table; Submitted to IEEE Signal Processing Letter
Summarization of Films and Documentaries Based on Subtitles and Scripts
We assess the performance of generic text summarization algorithms applied to
films and documentaries, using the well-known behavior of summarization of news
articles as reference. We use three datasets: (i) news articles, (ii) film
scripts and subtitles, and (iii) documentary subtitles. Standard ROUGE metrics
are used for comparing generated summaries against news abstracts, plot
summaries, and synopses. We show that the best performing algorithms are LSA,
for news articles and documentaries, and LexRank and Support Sets, for films.
Despite the different nature of films and documentaries, their relative
behavior is in accordance with that obtained for news articles.Comment: 7 pages, 9 tables, 4 figures, submitted to Pattern Recognition
Letters (Elsevier
Using Generic Summarization to Improve Music Information Retrieval Tasks
In order to satisfy processing time constraints, many MIR tasks process only
a segment of the whole music signal. This practice may lead to decreasing
performance, since the most important information for the tasks may not be in
those processed segments. In this paper, we leverage generic summarization
algorithms, previously applied to text and speech summarization, to summarize
items in music datasets. These algorithms build summaries, that are both
concise and diverse, by selecting appropriate segments from the input signal
which makes them good candidates to summarize music as well. We evaluate the
summarization process on binary and multiclass music genre classification
tasks, by comparing the performance obtained using summarized datasets against
the performances obtained using continuous segments (which is the traditional
method used for addressing the previously mentioned time constraints) and full
songs of the same original dataset. We show that GRASSHOPPER, LexRank, LSA,
MMR, and a Support Sets-based Centrality model improve classification
performance when compared to selected 30-second baselines. We also show that
summarized datasets lead to a classification performance whose difference is
not statistically significant from using full songs. Furthermore, we make an
argument stating the advantages of sharing summarized datasets for future MIR
research.Comment: 24 pages, 10 tables; Submitted to IEEE/ACM Transactions on Audio,
Speech and Language Processin
Query-Focused Multi-Document Summarization Using Co-Training Based Semi-Supervised Learning
PACLIC 23 / City University of Hong Kong / 3-5 December 200
Leveraging interfaces to improve recommendation diversity
Increasing diversity in the output of a recommender system is an active research question for solving a long-tail issue. Most of the current approaches have focused on ranked list optimization to improve recommendation diversity. However, little is known about the e.ect that a visual interface can have on this issue. .is paper shows that a multidimensional visualization promotes diversity of social exploration in the context of an academic conference. Our study shows a significant difference in the exploration pa.ern between ranked list and visual interfaces. .e results show that a visual interface can help the user explore a a more diverse set of recommended items