Teacher: 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.
Teacher: Now, understand the problem? If you are still confused, see the following example:
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.
Solution: A Neural Local Coherence Model
Reason: 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

Now, solve this instance: The problem of solving (n 1)-puzzle and cooperative path-finding (CPF) sub-optimally by rule based algorithms is addressed in this manuscript. The task in the puzzle is to rearrange n 1 pebbles on the square grid of the size of nn using one vacant position to a desired goal configuration. An improvement to the existent polynomial-time algorithm is proposed and experimentally analyzed. The improved algorithm is trying to move pebbles in a more efficient way than the original algorithm by grouping them into so-called snakes and moving them jointly within the snake. An experimental evaluation showed that the algorithm using snakes produces solutions that are 8% to 9% shorter than solutions generated by the original algorithm. The snake-based relocation has been also integrated into rule-based algorithms for solving the CPF problem sub-optimally, which is a closely related task. The task in CPF is to relocate a group of abstract robots that move over an undirected graph to given goal vertices. Robots can move to unoccupied neighboring vertices and at most one robot can be placed in each vertex. The (n 1)-puzzle is a special case of CPF where the underlying graph is represented by a 4-connected grid and there is only one vacant vertex. Two major rule-based algorithms for CPF were included in our study BIBOX and PUSH-and-SWAP (PUSH-and-ROTATE). Improvements gained by using snakes in the BIBOX algorithm were stable around 30% in (n 1)-puzzle solving and up to 50% in CPFs over bi-connected graphs with various ear decompositions and multiple vacant vertices. In the case of the PUSHand-SWAP algorithm the improvement achieved by snakes was around 5% to 8%. However, the improvement was unstable and hardly predictable in the case of PUSH-and-SWAP.
Student:
Improvements in Sub-optimal Solving of the (N 1)-Puzzle via Joint Relocation of Pebbles and its Applications to Rule-based Cooperative Path-Finding