- Phonology
- Morphology
- Lexis
- Syntax
- Semantics
- Discourse
- Pragmatics
- Dialogue
- Mathematical Foundations: Formal Grammars and Languages
- Finite-State Technology
- Statistical Models for Natural Language Processing
- Machine Learning
- Word Representation
- Deep Learning
- Similarity
- Evaluation
- Sublanguages and Controlled Languages
- Lexicography
- Corpora
- Corpus Annotation
- Ontologies
- Text Segmentation
- Part-of-Speech Tagging
- Parsing
- Semantic Role Labelling
- Word Sense Disambiguation
- Computational Treatment of Multiword Expressions
- Textual Entailment
- Natural Language Generation
- Speech Recognition
- Temporal Processing
- Text-to-Speech Synthesis
- Machine Translation
- Translation Technology
- Information Retrieval
- Information Extraction
- Question Answering
- Text Summarization
- Term Extraction
- Web Text Mining
- Opinion Mining and Sentiment Analysis
- Spoken Language Dialogue Systems
- Multimodal Systems
- Natural Language Processing for Educational Applications
- Automated Writing Assistance
- Text Simplification
- Author Profiling and Related Applications
Abstract and Keywords
Automated text summarization systems seek to provide the most important content contained in their (single or multiple document, and static or streaming over time) input. Extractive summarizers use various methods to assign an importance score to each fragment of the input and return the highest-scoring fragments, while abstractive summarizers attempt to compress and reformulate the extracted fragments and regenerate them in original and more elegant and coherent form. Numerous methods of scoring, combination, and compression have been developed. Evaluating the linguistic quality of a summary is much easier than evaluating the adequacy of its content. Various methods have been developed to compare system summary contents to the summaries of humans.
Keywords: automated text summarization, extract[ive] summary, abstract, maximal marginal relevance, ROUGE evaluation, Pyramid Method, summary content unit
Eduard Hovy heads the Natural Language Group at the Information Sciences Institute of the University of Southern California, and is a Research Associate Professor of the Computer Science Departments of USC and the University of Waterloo.
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- Phonology
- Morphology
- Lexis
- Syntax
- Semantics
- Discourse
- Pragmatics
- Dialogue
- Mathematical Foundations: Formal Grammars and Languages
- Finite-State Technology
- Statistical Models for Natural Language Processing
- Machine Learning
- Word Representation
- Deep Learning
- Similarity
- Evaluation
- Sublanguages and Controlled Languages
- Lexicography
- Corpora
- Corpus Annotation
- Ontologies
- Text Segmentation
- Part-of-Speech Tagging
- Parsing
- Semantic Role Labelling
- Word Sense Disambiguation
- Computational Treatment of Multiword Expressions
- Textual Entailment
- Natural Language Generation
- Speech Recognition
- Temporal Processing
- Text-to-Speech Synthesis
- Machine Translation
- Translation Technology
- Information Retrieval
- Information Extraction
- Question Answering
- Text Summarization
- Term Extraction
- Web Text Mining
- Opinion Mining and Sentiment Analysis
- Spoken Language Dialogue Systems
- Multimodal Systems
- Natural Language Processing for Educational Applications
- Automated Writing Assistance
- Text Simplification
- Author Profiling and Related Applications
