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Text Summarization
Text Summarization is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form.
There are different approaches to text summarization, including extractive methods that identify and extract important sentences or phrases from the text, and abstractive methods that generate new text based on the content of the original text.
하위 태스크
AT
Abstractive Text Summarization
DS
Document Summarization
LN
Long-Form Narrative Summarization
ET
Extractive Text Summarization
MS
Multi-Document Summarization
UO
Unsupervised Opinion Summarization
SC
Sentence Compression
SD
Scientific Document Summarization
QE
Query-Based Extractive Summarization
SC
Summarization Consistency Evaluation
OS
Opinion Summarization
SS
Sentence Summarization