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.

In many domains it is desirable to assess the pref erences of users in a qualitative rather than quan titative way. Such representations of qualitative preference orderings form an important compo nent of automated decision tools. We propose a graphical representation of preferences that re flects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a rep resentation is often compact and arguably natural. We describe several search algorithms for domi nance testing based on this representation; these algorithms are quite effective, especially in spe cific network topologies, such as chainand tree structured networks, as well as polytrees.
Reasoning With Conditional Ceteris Paribus Preference Statements

We consider the problem of using sentence compression techniques to facilitate queryfocused multi-document summarization. We present a sentence-compression-based framework for the task, and design a series of learning-based compression models built on parse trees. An innovative beam search decoder is proposed to efficiently find highly probable compressions. Under this framework, we show how to integrate various indicative metrics such as linguistic motivation and query relevance into the compression process by deriving a novel formulation of a compression scoring function. Our best model achieves statistically significant improvement over the state-of-the-art systems on several metrics (e.g. 8.0% and 5.4% improvements in ROUGE-2 respectively) for the DUC 2006 and 2007 summarization task.
A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization

Radiomics has proven to be a powerful prognostic tool for cancer detection, and has previously been applied in lung, breast, prostate, and head-and-neck cancer studies with great success. However, these radiomics-driven methods rely on pre-defined, hand-crafted radiomic feature sets that can limit their ability to characterize unique cancer traits. In this study, we introduce a novel discovery radiomics framework where we directly discover custom radiomic features from the wealth of available medical imaging data. In particular, we leverage novel StochasticNet radiomic sequencers for extracting custom radiomic features tailored for characterizing unique cancer tissue phenotype. Using StochasticNet radiomic sequencers discovered using a wealth of lung CT data, we perform binary classification on 42,340 lung lesions obtained from the CT scans of 93 patients in the LIDC-IDRI dataset. Preliminary results show significant improvement over previous state-of-the-art methods, indicating the potential of the proposed discovery radiomics framework for improving cancer screening and diagnosis.
Discovery Radiomics via StochasticNet Sequencers for Cancer Detection