Detailed Instructions: 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.
See one example below:
Problem: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
Solution: A Neural Local Coherence Model
Explanation: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage

Problem: The goal of open information extraction (OIE) is to extract surface relations and their arguments from naturallanguage text in an unsupervised, domainindependent manner. In this paper, we explore how overly-specific extractions can be reduced in OIE systems without producing uninformative or inaccurate results. We propose MinIE, an OIE system that produces minimized, annotated extractions. At its heart, MinIE rewrites OIE extractions by (1) identifying and removing parts that are considered overly specific; (2) representing information about polarity, modality, attribution, and quantities with suitable annotations instead of in the actual extraction. We conducted an experimental study with several real-world datasets and found that MinIE achieves competitive or higher precision and recall than most prior systems, while at the same time producing much shorter extractions.
Solution:
MinIE: Minimizing Facts in Open Information Extraction