You will be shown an abstract from a computer science research paper. Given this abstract, your task is to extract all unique entities with the following types:

- "Task": Applications, problems to solve, systems to construct. Examples include "information extraction", "machine reading system", "image segmentation".
- "Method": : Methods, models, systems to use, or tools, components of a system, frameworks. Examples include "language model", "CORENLP", "POS parser".
- "Metric": Metrics, measures, or entities that can express quality of a system / method. Examples include "F1", "BLEU", "Precision", "time complexity".
- "Material": Data, datasets, resources, Corpus, Knowledge base. Examples include "image data", "speech data", "stereo images", "CoNLL", "Wikipedia".
- "OtherScientificTerm": Phrases that are a scientific terms but do not fall into any of the above classes. Examples include "physical or geometric constraints", "qualitative prior knowledge", "tree kernel", "noise".
- "Generic": General terms or pronouns that may refer to a entity but are not themselves informative, often used as connection words. Examples include "model", "approach", "them".

Please return the output as a JSON object of the format: {"type1" : ["example_entity", ...], "type2" : ["example_entity", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. Entity types with no matching entities should be assigned an empty array [].

For instance, the output might look like: {"Task": ["speech recognition", ...], "Method": ["Conditional random field"], "Material": [], ...}.

Only output the JSON object and do not include any additional text.

Abstract:

This paper reports recent research into methods for creating natural language text. A new processing paradigm called Fragment-and-Compose has been created and an experimental system implemented in it. The knowledge to be expressed in text is first divided into small propositional units, which are then composed into appropriate combinations and converted into text. KDS (Knowledge Delivery System), which embodies this paradigm, has distinct parts devoted to creation of the propositional units, to organization of the text, to prevention of excess redundancy, to creation of combinations of units, to evaluation of these combinations as potential sentences, to selection of the best among competing combinations, and to creation of the final text. The Fragment-and-Compose paradigm and the computational methods of KDS are described.
{"Generic": ["methods", "computational methods"], "Material": [], "Method": ["KDS (Knowledge Delivery System)"], "Metric": [], "OtherScientificTerm": [], "Task": ["creating natural language text"]}