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: 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.
Example Output: Reasoning With Conditional Ceteris Paribus Preference Statements

Example Input: We provide a general framework for computing lower-bounds on the sample complexity of recovering the underlying graphs of Ising models, given i.i.d. samples. While there have been recent results for specific graph classes, these involve fairly extensive technical arguments that are specialized to each specific graph class. In contrast, we isolate two key graph-structural ingredients that can then be used to specify sample complexity lower-bounds. Presence of these structural properties makes the graph class hard to learn. We derive corollaries of our main result that not only recover existing recent results, but also provide lower bounds for novel graph classes not considered previously. We also extend our framework to the random graph setting and derive corollaries for Erdos-Renyi graphs in a certain dense setting.
Example Output: On the Information Theoretic Limits of Learning Ising Models

Example Input: This article describes a software module called Akshara to Prosodeme (A2P) converter in Hindi. It converts an input grapheme into prosedeme (sequence of phonemes with the specification of syllable boundaries and prosodic labels). The software is based on two proposed finite state machinesone for the syllabification and another for the syllable labeling. In addition to that, it also uses a set of nonlinear phonological rules proposed for foot formation in Hindi, which encompass solutions to schwa-deletion in simple, compound, derived and inflected words. The nonlinear phonological rules are based on metrical phonology with the provision of recursive foot structure. A software module is implemented in Python. The testing of the software for syllabification, syllable labeling, schwa deletion and prosodic labeling yield an accuracy of more than 99% on a lexicon of size 28664 words.
Example Output:
A Finite State and Rule-based Akshara to Prosodeme (A2P) Converter in Hindi