In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: 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.
[A]: On the Application of Generic Summarization Algorithms to Music


[Q]: This paper presents an empirical study of two widely-used sequence prediction models, Conditional Random Fields (CRFs) and Long Short-Term Memory Networks (LSTMs), on two fundamental tasks for Vietnamese text processing, including part-of-speech tagging and named entity recognition. We show that a strong lower bound for labeling accuracy can be obtained by relying only on simple word-based features with minimal handcrafted feature engineering, of 90.65% and 86.03% performance scores on the standard test sets for the two tasks respectively. In particular, we demonstrate empirically the surprising efficiency of word embeddings in both of the two tasks, with both of the two models. We point out that the state-of-the-art LSTMs model does not always outperform significantly the traditional CRFs model, especially on moderate-sized data sets. Finally, we give some suggestions and discussions for efficient use of sequence labeling models in practical applications.
[A]: An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing


[Q]: In this paper we propose a multi-convex framework for multi-task learning that improves predictions by learning relationships both between tasks and between features. Our framework is a generalization of related methods in multi-task learning, that either learn task relationships, or feature relationships, but not both. We start with a hierarchical Bayesian model, and use the empirical Bayes method to transform the underlying inference problem into a multi-convex optimization problem. We propose a coordinate-wise minimization algorithm that has a closed form solution for each block subproblem. Naively these solutions would be expensive to compute, but by using the theory of doubly stochastic matrices, we are able to reduce the underlying matrix optimization subproblem into a minimum weight perfect matching problem on a complete bipartite graph, and solve it analytically and efficiently. To solve the weight learning subproblem, we propose three different strategies, including a gradient descent method with linear convergence guarantee when the instances are not shared by multiple tasks, and a numerical solution based on Sylvester equation when instances are shared. We demonstrate the efficiency of our method on both synthetic datasets and real-world datasets. Experiments show that the proposed optimization method is orders of magnitude faster than an off-the-shelf projected gradient method, and our model is able to exploit the correlation structures among multiple tasks and features.
[A]:
Efficient Multi-task Feature and Relationship Learning