Automatic summarization

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Explore the main themes, entities and connections around Automatic summarization. Start with the topic map, then use the sections below for research and deeper semantic analysis.

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Applications and systems for summarization

Cluster Multi-document summarization Image collection exploration Submodular set function Determinantal point process

23 related topics

History

Hans Peter Luhn Term frequency–inverse document frequency Latent semantic analysis Non-negative matrix factorization Transformer models

7 related topics

Overview

Summary Artificial intelligence Algorithms Text NLP

40 related topics

Evaluation

Coherence ROUGE NIST N-gram Anaphor resolution

5 related topics

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Summary Abstract (summary)
Artificial intelligence
Algorithms Algorithm
Text Plain text
NLP Natural language processing
Computer vision
Image
Video synopsis
Supervised machine learning
Unigrams Unigram
Bigram
Binary classification
Precision and recall
Naive Bayes
Genetic algorithm
Training data Training set
PageRank
Social networks Social network
Graph Graph (abstract data type)
Lexical Lexical (semiotics)
Similarity Semantic similarity
Random surfing model
Eigenvalue
Stationary distribution
Random walk
Co-occurrence
Cohesion Cohesion (linguistics)
TNO Netherlands Organisation for Applied Scientific Research
Naive Bayes classifier
Maximum entropy Maximum entropy classifier

Commercial products

Google Docs

Approaches

Extraction Information extraction
Paraphrasing Automated paraphrasing

Applications and systems for summarization

Cluster Cluster analysis
Multi-document summarization
Image collection exploration
Submodular set function
Determinantal point process
Research articles Research article
Understanding of the text Natural-language understanding
Information retrieval
Full-text search
Keyword extraction
Combinatorial optimization
Set cover problem
Facility location problem Optimal facility location
Greedy algorithm
Reddit
Bot Internet bot
TL;DR
Internet slang
Adversarial stylometry
Podcasts Podcast
Speech recognition
Large language model
Speech synthesis

Evaluation

Coherence Coherence (linguistics)
ROUGE ROUGE (metric)
NIST
N-gram
Anaphor resolution Anaphora (linguistics)

History

Hans Peter Luhn
Term frequency–inverse document frequency
Latent semantic analysis
Non-negative matrix factorization
Transformer models Transformer (machine learning model)
RNN Rnn (software)
LSTM

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

Nodes85

Edges84

Triples112

Avg. degree1.98

Density0.023529

Components1

How this topic connects Entity context

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Automatic summarization

Top relations

related to Further reading · 87

Automatic summarization → Abderrafih, ACM Digital Library, Advances, Alexander, Alrehamy, Andrew, Andrew Goldberg, Angheluta, Anne, Annie, Archived, Automatic Summarization Task, AutoSummarize, Brigitte, Buist, Challenging Issues, CID Paris, Computational Intelligence Systems, Computer Science, Computing

has application · 9

Automatic summarization → Adversarial, Automatic, DR, Internet, It, Specific, The, The Reddit, TL

related to Recent approaches · 6

Automatic summarization → LSTM, Pegasus, Recently, RNN, T5, This

related to Commercial products · 2

Automatic summarization → Google Docs, In

is a · 1

Automatic summarization → process of shortening a set of data computationally

related to Approaches · 1

Automatic summarization → There

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

summarization text extraction keyphrases document automatic sentences summary summaries submodular keyphrase also example using used original content algorithms textrank algorithm

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
Automatic summarization	is a	process of shortening a set of data computationally	0.90	text
podcasts	instance of	if the detail lost is not major and the summary is sufficiently stylistically different to the input.Automatic summarization has been extended to audio content	0.80	text
where systems combine speech recognition	instance of	if the detail lost is not major and the summary is sufficiently stylistically different to the input.Automatic summarization has been extended to audio content	0.80	text
large language model summarization	instance of	if the detail lost is not major and the summary is sufficiently stylistically different to the input.Automatic summarization has been extended to audio content	0.80	text
and speech synthesis to produce condensed narrated audio summaries of full-length episodes	instance of	if the detail lost is not major and the summary is sufficiently stylistically different to the input.Automatic summarization has been extended to audio content	0.80	text
T5	instance of	This includes models	0.80	text
Pegasus	instance of	This includes models	0.80	text
Automatic summarization	has application	Specific	0.60	section
Automatic summarization	has application	The Reddit	0.60	section
Automatic summarization	has application	It	0.60	section
Automatic summarization	has application	The	0.60	section
Automatic summarization	has application	TL	0.60	section

Related concept clusters Concept neighborhoods

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Connections between topic areas Semantic bridges

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Min side: 3

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Applications and systems for summarization

History

Overview

Evaluation

Key facts & relationships

Topics to explore

Overview

Commercial products

Approaches

Applications and systems for summarization

Evaluation

History

Advanced semantic analysis

Map overview Semantic statistics

Automatic summarization

How this topic connects Entity context

Automatic summarization

Top relations

Important terminology Word statistics

Important terminology

Entity relationships Subject–Predicate–Object triples

Related concept clusters Concept neighborhoods

Connections between topic areas Semantic bridges