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]: Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language. In this paper, we pose the problem of multimodal sentiment analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed approach is tailored for the volatile nature of spoken language in online videos as well as accompanying gestures and voice. In the experiments, our model outperforms state-ofthe-art approaches for both multimodal and unimodal sentiment analysis.
[A]: Tensor Fusion Network for Multimodal Sentiment Analysis


[Q]: As food becomes an important part of modern life, recipes shared on the web are a great indicator of civilizations and culinary attitudes in different countries. Similarly, ingredients, flavors, and nutrition information are strong signals of the taste preferences of individuals from various parts of the world. Yet, we do not have a thorough understanding of these palate varieties. In this paper, we present a large-scale study of recipes published on the Web and their content, aiming to understand cuisines and culinary habits around the world. Using a database of more than 157K recipes from over 200 different cuisines, we analyze ingredients, flavors, and nutritional values which distinguish dishes from different regions, and use this knowledge to assess the predictability of recipes from different cuisines. We then use country health statistics to understand the relation between these factors and health indicators of different nations, such as obesity, diabetes, migration, and health expenditure. Our results confirm the strong effects of geographical and cultural similarities on recipes, health indicators, and culinary preferences between countries around the world.
[A]: Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web


[Q]: Supplier selection is a typical multi-criteria decision making(MCDM) problem and lots of uncertain information exist inevitably. To address this issue, a new method was proposed based on interval data fusion. Our method follows the original way to generate classical basic probability assignment(BPA) determined by the distance among the evidences. However, the weights of criteria are kept as interval numbers to generate interval BPAs and do the fusion of interval BPAs. Finally, the order is ranked and the decision is made according to the obtained interval BPAs. In this paper, a numerical example of supplier selection is applied to verify the feasibility and validity of our method. The new method is presented aiming at solving multiplecriteria decision-making problems in which the weights of criteria or experts are described in fuzzy data like linguistic terms or interval data.
[A]:
Evidential supplier selection based on interval data fusion