Carlos Colmenares
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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
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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.
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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.
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