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 Input:
The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques to solve Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field inception, the number of DCOP realistic applications and benchmark used to asses the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describe the problem of coordinating smart devices schedules across multiple homes as a multi-agent system, (ii) detail the physical models adopted to simulate smart sensors, smart actuators, and homes environments, and (iii) introduce a DCOP realistic benchmark for SHDS problems.

Ex Output:
A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs


Ex Input:
Multimedia or spoken content presents more attractive information than plain text content, but the former is more difficult to display on a screen and be selected by a user. As a result, accessing large collections of the former is much more difficult and time-consuming than the latter for humans. It’s therefore highly attractive to develop machines which can automatically understand spoken content and summarize the key information for humans to browse over. In this endeavor, a new task of machine comprehension of spoken content was proposed recently. The initial goal was defined as the listening comprehension test of TOEFL, a challenging academic English examination for English learners whose native languages are not English. An Attention-based Multi-hop Recurrent Neural Network (AMRNN) architecture was also proposed for this task, which considered only the sequential relationship within the speech utterances. In this paper, we propose a new Hierarchical Attention Model (HAM), which constructs multi-hopped attention mechanism over tree-structured rather than sequential representations for the utterances. Improved comprehension performance robust with respect to ASR errors were obtained.

Ex Output:
HIERARCHICAL ATTENTION MODEL FOR IMPROVED MACHINE COMPREHENSION OF SPOKEN CONTENT


Ex Input:
This paper presents results of our experiments using the Ubuntu Dialog Corpus –<lb>the largest publicly available multi-turn dialog corpus. First, we use an in-house<lb>implementation of previously reported models to do an independent evaluation<lb>using the same data. Second, we evaluate the performances of various LSTMs,<lb>Bi-LSTMs and CNNs on the dataset. Third, we create an ensemble by averaging<lb>predictions of multiple models. The ensemble further improves the performance<lb>and it achieves a state-of-the-art result for this dataset. Finally, we discuss our<lb>future plans using this corpus.

Ex Output:

Improved Deep Learning Baselines for Ubuntu Corpus Dialogs