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.
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Question: Several large cloze-style context-questionanswer datasets have been introduced recently: the CNN and Daily Mail news data and the Children’s Book Test. Thanks to the size of these datasets, the associated text comprehension task is well suited for deep-learning techniques that currently seem to outperform all alternative approaches. We present a new, simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models. This makes the model particularly suitable for question-answering problems where the answer is a single word from the document. Our model outperforms models previously proposed for these tasks by a large margin.

Answer: Text Understanding with the Attention Sum Reader Network


Question: The majority of online display ads are served through realtime bidding (RTB) — each ad display impression is auctioned off in real-time when it is just being generated from a user visit. To place an ad automatically and optimally, it is critical for advertisers to devise a learning algorithm to cleverly bid an ad impression in real-time. Most previous works consider the bid decision as a static optimization problem of either treating the value of each impression independently or setting a bid price to each segment of ad volume. However, the bidding for a given ad campaign would repeatedly happen during its life span before the budget runs out. As such, each bid is strategically correlated by the constrained budget and the overall effectiveness of the campaign (e.g., the rewards from generated clicks), which is only observed after the campaign has completed. Thus, it is of great interest to devise an optimal bidding strategy sequentially so that the campaign budget can be dynamically allocated across all the available impressions on the basis of both the immediate and future rewards. In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign’s real-time parameters, while an action is the bid price to set. By modeling the state transition via auction competition, we build a Markov Decision Process framework for learning the optimal bidding policy to optimize the advertising performance in the dynamic real-time bidding environment. Furthermore, the scalability problem from the large real-world auction volume and campaign budget is well handled by state value approximation using neural networks. The empirical study on two large-scale real-world datasets and the live A/B testing on a commercial platform have demonstrated the superior performance and high efficiency compared to state-of-the-art methods.

Answer: Real-Time Bidding by Reinforcement Learning in Display Advertising


Question: Semi-supervised learning based on the low-density separation principle such as<lb>the cluster and manifold assumptions has been extensively studied in the last<lb>decades. However, such semi-supervised learning methods do not always per-<lb>form well due to violation of the cluster and manifold assumptions. In this paper,<lb>we propose a novel approach to semi-supervised learning that does not require<lb>such restrictive assumptions. Our key idea is to combine learning from positive<lb>and negative data (standard supervised learning) and learning from positive and<lb>unlabeled data (PU learning), the latter is guaranteed to be able to utilize unla-<lb>beled data without the cluster and manifold assumptions. We theoretically and<lb>experimentally show the usefulness of our approach.

Answer:
Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning