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:
A new approach called SKYSET (Synthetic Knowledge Yield Social Entities Translation) is proposed to validate completeness and to reduce ambiguity from written instructional documentation. SKYSET utilizes a quintuple set of standardized categories, which differs from traditional approaches that typically use triples. The SKYSET System defines the categories required to form a standard template for representing information that is portable across different domains. It provides a standardized framework that enables sentences from written instructions to be translated into sets of category typed entities on a table or database. The SKYSET entities contain conceptual units or phrases that represent information from the original source documentation. SKYSET enables information concatenation where multiple documents from different domains can be translated and combined into a single common filterable and searchable table of entities. Index Terms – Cognitive Linguistics, Dual Intentional Semantic Decomposition and Reconstruction (DISDR), Frame Semantics, Information Concatenation, Information Integration, Information Summarization, Knowledge Architecture, Knowledge Management Internal External (KMIE), Metalinguistic Awareness, Synthetic Knowledge Yield Social Entities Translation (SKYSET).

Ex Output:
Introducing SKYSET - a Quintuple Approach for Improving Instructions


Ex Input:
-This study proposes a framework of Uncertainty-based Group Decision Support System (UGDSS). It provides a platform for multiple criteria decision analysis in six aspects including (1) decision environment, (2) decision problem, (3) decision group, (4) decision conflict, (5) decision schemes and (6) group negotiation. Based on multiple artificial intelligent technologies, this framework provides reliable support for the comprehensive manipulation of applications and advanced decision approaches through the design of an integrated multi-agents architecture.

Ex Output:
Towards a Reliable Framework of Uncertainty-based Group Decision Support System


Ex Input:
Argument Component Boundary Detection (ACBD) is an important sub-task in argumentation mining; it aims at identifying the word sequences that constitute argument components, and is usually considered as the first sub-task in the argumentation mining pipeline. Existing ACBD methods heavily depend on task-specific knowledge, and require considerable human efforts on feature-engineering. To tackle these problems, in this work, we formulate ACBD as a sequence labeling problem and propose a variety of Recurrent Neural Network (RNN) based methods, which do not use domain specific or handcrafted features beyond the relative position of the sentence in the document. In particular, we propose a novel joint RNN model that can predict whether sentences are argumentative or not, and use the predicted results to more precisely detect the argument component boundaries. We evaluate our techniques on two corpora from two different genres; results suggest that our joint RNN model obtain the state-of-the-art performance on both datasets.

Ex Output:

Joint RNN Model for Argument Component Boundary Detection