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.
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Question: 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.

Answer: On the Application of Generic Summarization Algorithms to Music


Question: We introduce a batched lazy algorithm for supervised classification using decision trees. It avoids unnecessary visits to irrelevant nodes when it is used to make predictions with either eagerly or lazily trained decision trees. A set of experiments demonstrate that the proposed algorithm can outperform both the conventional and lazy decision tree algorithms in terms of computation time as well as memory consumption, without compromising accuracy.

Answer: Batched Lazy Decision Trees


Question: Image captioning has so far been explored mostly in English, as most available datasets are in this language. However, the application of image captioning should not be restricted by language. Only few studies have been conducted for image captioning in a cross-lingual seing. Dierent from these works that manually build a dataset for a target language, we aim to learn a cross-lingual captioning model fully from machine-translated sentences. To conquer the lack of uency in the translated sentences, we propose in this paper a uency-guided learning framework. e framework comprises a module to automatically estimate the uency of the sentences and another module to utilize the estimated uency scores to eectively train an image captioning model for the target language. As experiments on two bilingual (English-Chinese) datasets show, our approach improves both uency and relevance of the generated captions in Chinese, but without using any manually wrien sentences from the target language.

Answer:
Fluency-Guided Cross-Lingual Image Captioning