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.
One example is below.
Q: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
A: A Neural Local Coherence Model
Rationale: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage
Q: Symbolic approaches to deep parsing often require large-coverage and fine-grained lexical information, such as a syntactic lexicon. LexiconGrammar tables (Gross 1975, 1994), carefully developed by linguists since the 70s, constitute such a syntactic resource. Each table represents a class of predicates sharing some syntactic features. Each row corresponds to a lexical entry (verb, predicative noun, predicative adjective, adverb, fixed expression) and each column corresponds to a syntactic feature (construction, argument distribution, and so on). However, they are not directly exploitable for NLP applications because pieces of information are not formally encoded although their informal descriptions are available in the literature. Some projects such as (Hathout et Namer 1998, Gardent et al. 2006, Sagot et Fort 2007, Danlos et Sagot 2009) attempted to reformat LexiconGrammar tables in a lexicon for NLP. In these projects, each class is assigned a specific configuration which encodes missing information and defines restructuration operations. For instance, each configuration in (Gardent et al. 2006) is represented by a graph that makes the class structure explicit and translates each column header into a feature structure. Nevertheless, Lexicon-Grammar tables are continually updated to be improved (e.g., addition and renaming of features) and this approach can be tedious to maintain. For example, if a same feature is added to several classes, all corresponding configurations have to be modified. In this paper, we describe LGExtract, a tool that uses a global approach. First, it relies on the so-called table of classes, which encodes pieces of information that are undefined in the original classes, especially features that are constant over a whole class. Next, as a syntactic feature has exactly one interpretation over the set of classes, our extraction script assigns to each feature a set of reformatting operations once. This paper is organized as follows. First, we briefly describe the LexiconGrammar classes and the table of classes, and their relevance to our work. Then, we present LGExtract in detail, illustrate it with a concrete example for French and discuss its main advantages and drawbacks.
A:
A generic tool to generate a lexicon for NLP from Lexicon-Grammar tables