Carlos Colmenares

Carlos Colmenares

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    Sentence Compression by Deletion with LSTMs
    Lukasz Kaiser
    Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP'15)
    Preview abstract We present an LSTM approach to deletion-based sentence compression where the task is to translate a sentence into a sequence of zeros and ones, corresponding to token deletion decisions. We demonstrate that even the most basic version of the system, which is given no syntactic information (no PoS or NE tags, or dependencies) or desired compression length, performs surprisingly well: around 30% of the compressions from a large test set could be regenerated. We compare the LSTM system with a competitive baseline which is trained on the same amount of data but is additionally provided with all kinds of linguistic features. In an experiment with human raters the LSTM-based model outperforms the baseline achieving 4.5 in readability and 3.8 in informativeness. View details
    HEADS: Headline Generation as Sequence Prediction Using an Abstract Feature-Rich Space
    Marina Litvak
    Amin Mantrach
    Fabrizio Silvestri
    Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL (NAACL'15), pp. 133-142
    Preview abstract Automatic headline generation is a sub-task of document summarization with many reported applications. In this study we present a sequence-prediction technique for learning how editors title their news stories. The introduced technique models the problem as a discrete optimization task in a feature-rich space. In this space the global optimum can be found in polynomial time by means of dynamic programming. We train and test our model on an extensive corpus of financial news, and compare it against a number of baselines by using standard metrics from the document summarization domain, as well as some new ones proposed in this work. We also assess the readability and informativeness of the generated titles through human evaluation. The obtained results are very appealing and substantiate the soundness of the approach. View details
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