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.
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Question: Mental health forums are online communities where people express their issues and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential selfharm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We present a framework for triaging user content into four severity categories which are defined based on indications of self-harm ideation. Our models are based on a feature-rich classification framework which includes lexical, psycholinguistic, contextual and topic modeling features. Our approaches improve the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Using the proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate a decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in

Answer: Triaging Content Severity in Online Mental Health Forums∗


Question: 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).

Answer: Introducing SKYSET - a Quintuple Approach for Improving Instructions


Question: In this work we present a method for using Deep Q-Networks (DQNs) in multi-objective tasks. Deep Q-Networks provide remarkable performance in single objective tasks learning from high-level visual perception. However, in many scenarios (e.g in robotics), the agent needs to pursue multiple objectives simultaneously. We propose an architecture in which separate DQNs are used to control the agent’s behaviour with respect to particular objectives. In this architecture we use signal suppression, known from the (Brooks) subsumption architecture, to combine outputs of several DQNs into a single action. Our architecture enables the decomposition of the agent’s behaviour into controllable and replaceable sub-behaviours learned by distinct modules. To evaluate our solution we used a game-like simulator in which an agent provided with high-level visual input pursues multiple objectives in a 2D world. Our solution provides benefits of modularity, while its performance is comparable to the monolithic approach.

Answer:
Multi-Objective Deep Q-Learning with Subsumption Architecture