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]: Passengers’ experience is becoming a key metric to evaluate the air transportation system’s performance. Efficient and robust tools to handle airport operations are needed along with a better understanding of passengers’ interests and concerns. Among various airport operations, this paper studies airport gate scheduling for improved passengers’ experience. Three objectives accounting for passengers, aircraft, and operation are presented. Trade-offs between these objectives are analyzed, and a balancing objective function is proposed. The results show that the balanced objective can improve the efficiency of traffic flow in passenger terminals and on ramps, as well as the robustness of gate operations.
[EX A]: Airport Gate Scheduling for Passengers, Aircraft, and Operation

[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]: Research on Symbolic Probabilistic Inference (SPI) [2, 3) has provided an algorithm for re­ solving general queries in Bayesian networks. SPI applies the concept of dependency­ directed backward search to probabilistic in­ ference, and is incremental with respect to both queries and observations. Unlike tra­ ditional Bayesian network inferencing algo­ rithms, SPI algorithm is goal directed, per­ forming only those calculations that are re­ quired to respond to queries. Research to date on SPI applies to Bayesian networks with discrete-valued variables and does not address variables with continuous values. In this paper1, we extend the SPI algorithm to handle Bayesian networks made up of continuous variables where the relationships between the variables are restricted to be "linear gaussian". We call this variation of the SPI algorithm, SPI Continuous (SPIC). SPIC modifies the three basic SPI opera­ tions: multiplication, summation, and sub­ stitution. However, SPIC retains the frame­ work of the SPI algorithm, namely building the search tree and recursive query mecha­ nism and therefore retains the goal-directed and incrementality features of SPI.
[EX A]:
Symbolic Probabilistic Inference with Continuous Variables