Liu et al., 2022 - Google Patents
SEASum: Syntax-enriched abstractive summarizationLiu et al., 2022
- Document ID
- 11770966670119917384
- Author
- Liu S
- Yang L
- Cai X
- Publication year
- Publication venue
- Expert Systems with Applications
External Links
Snippet
Compared to traditional RNN-based models, abstractive summarization systems based on Pre-trained Language Models (PTMs) achieve dramatic improvements in readability. Thus, in the field of abstractive summarization, more attention should be devoted to the faithfulness …
- 230000003935 attention 0 abstract description 35
Classifications
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- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
- G06F17/30657—Query processing
- G06F17/30675—Query execution
- G06F17/30684—Query execution using natural language analysis
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- G—PHYSICS
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- G06F17/271—Syntactic parsing, e.g. based on context-free grammar [CFG], unification grammars
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- G06F17/2765—Recognition
- G06F17/277—Lexical analysis, e.g. tokenisation, collocates
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- G06F17/279—Discourse representation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
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- G06F17/2809—Data driven translation
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