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.
One example is below.
Q: 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.
A: A Neural Local Coherence Model
Rationale: 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
Q: Selecting the right web links for a website is important because appropriate links not only can provide high attractiveness but can also increase the website's revenue. In this work, we first show that web links have an intrinsic multilevel feedback structure. For example, consider a 2-level feedback web link: the 1st level feedback provides the Click-Through Rate (CTR) and the 2nd level feedback provides the potential revenue, which collectively produce the compound 2-level revenue. We consider the context-free links selection problem of selecting links for a homepage so as to maximize the total compound 2-level revenue while keeping the total 1st level feedback above a preset threshold. We further generalize the problem to links with n (n2)-level feedback structure. To our best knowledge, we are the first to model the links selection problem as a constrained multi-armed bandit problem and design an effective links selection algorithm by learning the links' multi-level structure with provable sub-linear regret and violation bounds. We uncover the multi-level feedback structures of web links in two real-world datasets. We also conduct extensive experiments on the datasets to compare our proposed LExp algorithm with two state-of-the-art context-free bandit algorithms and show that LExp algorithm is the most effective in links selection while satisfying the constraint.
A:
Multi-level Feedback Web Links Selection Problem: Learning and Optimization