The bigger theĭifference in size between the summary and the original text, the harder the problem willīe since important information will be sparser and identifying them can be more difficult. Papers, which are somewhat small texts with simple and clear structure. Most researchĪbout automatic summarization revolves around summarizing news articles or scientific ItĬan be used to create the data needed to train different language models. %X Automatic text alignment is an important problem in natural language processing.
#PCA METHOD FOR HYPERIMAGE MOVIE#
%T AligNarr: Aligning Narratives of Different Length for Movie Summarization : International Max Planck Research School, MPI for Informatics, Max Planck Societyĭatabases and Information Systems, MPI for Informatics, Max Planck Society %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of = , Ascent combines open information extraction with judicious cleaning using language models. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. This thesis presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots.
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