TASK DEFINITION: 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.
PROBLEM: In this paper, we will expound upon the concepts proffered in [5], where we proposed an information theoretic approach to intelligence in the computational sense. We will examine data and meme aggregation, and study the effect of limited resources on the resulting meme amplitudes.

SOLUTION: The Computational Theory of Intelligence: Data Aggregation

PROBLEM: We show how eye-tracking corpora can be used to improve sentence compression models, presenting a novel multi-task learning algorithm based on multi-layer LSTMs. We obtain performance competitive with or better than state-of-the-art approaches.

SOLUTION: Improving sentence compression by learning to predict gaze

PROBLEM: Recent work on embedding ontology concepts has relied on either expensive manual annotation or automated concept tagging methods that ignore the textual contexts around concepts. We propose a novel method for jointly learning concept, phrase, and word embeddings from an unlabeled text corpus, by using the representative phrases for ontology concepts as distant supervision. We learn embeddings for medical concepts in the Unified Medical Language System and generaldomain concepts in YAGO, using a variety of corpora. Our embeddings show performance competitive with existing methods on concept similarity and relatedness tasks, while requiring no human corpus annotation and demonstrating more than 3x coverage in the vocabulary size.

SOLUTION:
A Weakly-Supervised Method for Jointly Embedding Concepts, Phrases, and Words