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.

Ex Input:
Many genres of natural language text are narratively structured, a testament to our predilection for organizing our experiences as narratives. There is broad consensus that understanding a narrative requires identifying and tracking the goals and desires of the characters and their narrative outcomes. However, to date, there has been limited work on computational models for this problem. We introduce a new dataset, DesireDB, which includes goldstandard labels for identifying statements of desire, textual evidence for desire fulfillment, and annotations for whether the stated desire is fulfilled given the evidence in the narrative context. We report experiments on tracking desire fulfillment using different methods, and show that LSTM Skip-Thought model achieves F-measure of 0.7 on our corpus.

Ex Output:
Modelling Protagonist Goals and Desires in First-Person Narrative

Ex Input:
We develop a probabilistic latent-variable model to discover semantic framestypes of events and their participantsfrom corpora. We present a Dirichlet-multinomial model in which frames are latent categories that explain the linking of verb-subject-object triples, given document-level sparsity. We analyze what the model learns, and compare it to FrameNet, noting it learns some novel and interesting frames. This document also contains a discussion of inference issues, including concentration parameter learning; and a small-scale error analysis of syntactic parsing accuracy. Note: this work was originally posted online October 2012 as part of CMU MLD's Data Analysis Project requirement. This version has no new experiments or results, but has added some discussion of new related work.

Ex Output:
Learning Frames from Text with an Unsupervised Latent Variable Model

Ex Input:
This report presents a general model of the architecture of information systems for the children's speech recognition. It presents a model of the speech data stream and how it works. The result of these studies and presented veins architectural model shows that research needs to be focused on acoustic-phonetic modeling in order to improve the quality of children's speech recognition and the sustainability of the systems to noise and changes in transmission environment. Another important aspect is the development of more accurate algorithms for modeling of spontaneous child speech.

Ex Output:
On model architecture for a children's speech recognition interactive dialog system