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.

[EX Q]: -This study proposes a framework of Uncertainty-based Group Decision Support System (UGDSS). It provides a platform for multiple criteria decision analysis in six aspects including (1) decision environment, (2) decision problem, (3) decision group, (4) decision conflict, (5) decision schemes and (6) group negotiation. Based on multiple artificial intelligent technologies, this framework provides reliable support for the comprehensive manipulation of applications and advanced decision approaches through the design of an integrated multi-agents architecture.
[EX A]: Towards a Reliable Framework of Uncertainty-based Group Decision Support System

[EX Q]: In this paper, we implicitly incorporate morpheme information into word embedding. Based on the strategy we utilize the morpheme information, three models are proposed. To test the performances of our models, we conduct the word similarity and syntactic analogy. The results demonstrate the effectiveness of our methods. Our models beat the comparative baselines on both tasks to a great extent. On the golden standard Wordsim-353 and RG-65, our models approximately outperform CBOW for 5 and 7 percent, respectively. In addition, 7 percent advantage is also achieved by our models on syntactic analysis. According to parameter analysis, our models can increase the semantic information in the corpus and our performances on the smallest corpus are similar to the performance of CBOW on the corpus which is five times ours. This property of our methods may have some positive effects on NLP researches about the corpus-limited languages.
[EX A]: Implicitly Incorporating Morphological Information into Word Embedding

[EX Q]: Embedding words in a vector space has gained a lot of research attention in re-<lb>cent years. While state-of-the-art methods provide efficient computation of word<lb>similarities via a low-dimensional matrix embedding, their motivation is often left<lb>unclear. In this paper, we argue that word embedding can be naturally viewed<lb>as a ranking problem. Then, based on this insight, we propose a novel frame-<lb>work WordRank that efficiently estimates word representations via robust ranking.<lb>The performance of WordRank is measured in word similarity and word analogy<lb>benchmarks, and the results are compared to the state-of-the-art word embedding<lb>techniques. Our algorithm produces a vector space with meaningful substructure,<lb>as evidenced by its performance of 77.4% accuracy on a popular word similarity<lb>benchmark and 76% on the Google word analogy benchmark. WordRank per-<lb>forms especially well on small corpora.
[EX A]:
WordRank: Learning Word Embeddings via Robust Ranking