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.

Example Input: Existing studies on semantic parsing mainly focus on the in-domain setting. We formulate cross-domain semantic parsing as a domain adaptation problem: train a semantic parser on some source domains and then adapt it to the target domain. Due to the diversity of logical forms in different domains, this problem presents unique and intriguing challenges. By converting logical forms into canonical utterances in natural language, we reduce semantic parsing to paraphrasing, and develop an attentive sequence-to-sequence paraphrase model that is general and flexible to adapt to different domains. We discover two problems, small micro variance and large macro variance, of pretrained word embeddings that hinder their direct use in neural networks, and propose standardization techniques as a remedy. On the popular OVERNIGHT dataset, which contains eight domains, we show that both cross-domain training and standardized pre-trained word embedding can bring significant improvement.
Example Output: Cross-domain Semantic Parsing via Paraphrasing

Example Input: Our study identifies sentences in Wikipedia articles that are either identical or highly similar by applying techniques for near-duplicate detection of web pages. This is accomplished with a MapReduce implementation of minhash to identify clusters of sentences with high Jaccard similarity. We show that these clusters can be categorized into six different types, two of which are particularly interesting: identical sentences quantify the extent to which content in Wikipedia is copied and pasted, and near-duplicate sentences that state contradictory facts point to quality issues in Wikipedia.
Example Output: Identifying Duplicate and Contradictory Information in Wikipedia

Example Input: Wide-angle sonar mapping of the environ ment by mobile robot is nontrivial due to sev eral sources of uncertainty: dropouts due to "specular" reflections, obstacle location un certainty due to the wide beam, and distance measurement error. Earlier papers address the latter problems, but dropouts remain a problem in many environments. We present an approach that lifts the overoptimistic in dependence assumption used in earlier work, and use Bayes nets to represent the depen dencies between objects of the model. Ob jects of the model consist of readings, and of regions in which "quasi location invari ance" of the (possible) obstacles exists, with respect to the readings. Simulation supports the method's feasibility. The model is readily extensible to allow for prior distributions, as well as other types of sensing operations.
Example Output:
Bayes Networks for Sonar Sensor Fusion