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.

Input: Consider Input: One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data. In this work, we study the task of identifying products being bought and sold in online cybercrime forums, which exhibits particularly challenging cross-domain effects. We formulate a task that represents a hybrid of slot-filling information extraction and named entity recognition and annotate data from four different forums. Each of these forums constitutes its own “fine-grained domain” in that the forums cover different market sectors with different properties, even though all forums are in the broad domain of cybercrime. We characterize these domain differences in the context of a learning-based system: supervised models see decreased accuracy when applied to new forums, and standard techniques for semi-supervised learning and domain adaptation have limited effectiveness on this data, which suggests the need to improve these techniques. We release a dataset of 1,938 annotated posts from across the four forums.1

Output: Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation


Input: Consider Input: In natural speech, the speaker does not pause between words, yet a human listener somehow perceives this continuous stream of phonemes as a series of distinct words. The detection of boundaries between spoken words is an instance of a general capability of the human neocortex to remember and to recognize recurring sequences. This paper describes a computer algorithm that is designed to solve the problem of locating word boundaries in blocks of English text from which the spaces have been removed. This problem avoids the complexities of processing speech but requires similar capabilities for detecting recurring sequences. The algorithm that is described in this paper relies entirely on statistical relationships between letters in the input stream to infer the locations of word boundaries. The source code for a C++ version of this algorithm is presented in an appendix.

Output: A Statistical Learning Algorithm for Word Segmentation


Input: Consider Input: Given the incessant growth of documents describing the opinions of different people circulating on the web, including Web 2.0 has made it possible to give an opinion on any product in the net. In this paper, we examine the various opinions expressed in the tweets and classify them (positive, negative or neutral) by using the emoticons for the Bayesian method and adjectives and adverbs for the Turney’s method.


Output: Detecting Opinions in Tweets