We present a new approach to automatic summarization based on neural nets, called NetSum. We extract a set of features from each sentence that helps identify its importance in the...
Krysta Marie Svore, Lucy Vanderwende, Christopher ...
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several de...
Breaking news often contains timely definitions and descriptions of current terms, organizations and personalities. We utilize such web sources to construct definitions for such t...
Measuring the similarity between semantic relations that hold among entities is an important and necessary step in various Web related tasks such as relation extraction, informati...