"Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content."
Reducing the length of a piece of text while retaining its essence.
Natural Language Processing (NLP): This refers to the field of computer science that deals with the processing of human language data. It involves tasks such as analyzing, understanding, and generating human language.
Machine Learning (ML): This refers to a subset of artificial intelligence (AI) that enables machines to automatically learn and improve from experience without being explicitly programmed.
Semantic Analysis: This refers to the process of understanding the meaning of words and sentences in a given text. It involves tasks such as named entity recognition, part-of-speech tagging, and sentiment analysis.
Text Classification: This refers to the process of categorizing a given text into predefined categories. It involves tasks such as topic modeling, document classification, and clustering.
Information Retrieval (IR): This refers to the process of retrieving relevant information from a large corpus of text. It involves tasks such as indexing, ranking, and querying.
Natural Language Generation (NLG): This refers to the process of generating human-like language from structured or unstructured data. It involves tasks such as text generation, summarization, and paraphrasing.
Neural Networks: This refers to a class of machine learning algorithms that is modelled after the structure of the human brain. It is used for tasks such as image recognition, speech recognition, and natural language processing.
Phrase Extraction: This refers to the process of identifying and extracting key phrases from a given document. It is important for text summarization, as the summary is often based on the most important phrases in the text.
Sentence Extraction: This refers to the process of identifying and extracting the most important sentences from a given document. It involves tasks such as sentence ranking and sentence clustering.
Text Compression: This refers to the process of reducing the size of a given text while retaining the most important information. Techniques used include encoding, truncation, and summarization.
Evaluation Metrics: This refers to the measures used to evaluate the quality of a text summarization system. Common metrics include ROUGE, BLEU, and F-score.
Deep Learning: This refers to a subset of machine learning that is based on artificial neural networks. It is used for tasks such as image recognition, speech recognition, and natural language processing.
Word Embeddings: This refers to the process of representing words as high-dimensional vectors in a semantic space. It is used for tasks such as word similarity, text classification, and entity recognition.
Sentiment Analysis: This refers to the process of identifying the emotional tone of a given text. It is important for tasks such as product reviews and social media analysis.
Entity Recognition: This refers to the process of identifying and categorizing different types of entities (such as people, organizations, and locations) in a given text. It is important for tasks such as information extraction and knowledge base construction.
Extractive summarization: This type of summarization involves selecting important sentences or phrases from the source text and generating a summary based on them.
Abstractive summarization: This type of summarization involves the generation of new sentences that capture the essence of the source text, rather than simply selecting existing text.
Hybrid summarization: This type of summarization combines elements of both extractive and abstractive summarization to generate a summary that is more fluent and coherent than either approach alone.
Sentence compression: This type of summarization involves reducing the length of sentences from the source text by removing extraneous words or phrases, while still maintaining the key information.
Keyword-based summarization: This type of summarization involves identifying the most important keywords from the source text and generating a summary based on those keywords.
Cluster-based summarization: This type of summarization groups similar sentences or phrases from the source text together and generates a summary based on the most representative sentences or phrases from each cluster.
Latent Semantic Analysis (LSA) summarization: This type of summarization involves analyzing the relationships between words in the source text and generating a summary based on the most important semantic concepts that are identified.
Graph-based summarization: This type of summarization involves representing the source text as a graph and using graph-based algorithms to identify the most important nodes (sentences or phrases) in the graph, which are then used to generate a summary.
Neural network-based summarization: This type of summarization uses deep learning techniques to train neural networks to generate summaries of source text.
Template-based summarization: This type of summarization involves the use of pre-defined templates to generate summaries that follow a specific structure or format.
"Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data."
"Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document."
"Visual content can be summarized using computer vision algorithms."
"Image summarization is the subject of ongoing research."
"Existing approaches typically attempt to display the most representative images from a given image collection, or generate a video that only includes the most important content from the entire collection."
"Video summarization algorithms identify and extract from the original video content the most important frames (key-frames), and/or the most important video segments (key-shots)."
"Video summaries simply retain a carefully selected subset of the original video frames and, therefore, are not identical to the output of video synopsis algorithms."
"To create a subset (a summary) that represents the most important or relevant information within the original content."
"Different types of data can be automatically summarized, such as text, visual content, and videos."
"Natural language processing methods are primarily used in text summarization."
"Existing approaches in image summarization aim to display the most representative images from a given image collection."
"Video summarization algorithms can extract the most important frames (key-frames) and/or the most important video segments (key-shots)."
"Video summaries are not identical to the output of video synopsis algorithms."
"AI algorithms are commonly developed and employed to achieve automatic summarization."
"Visual content can be summarized using computer vision algorithms."
"Existing approaches in image summarization generate a subset of the most important content from a given image collection."
"To locate the most informative sentences in a given document."
"Video summarization is normally organized in a temporally ordered fashion."
"Yes, automatic summarization is the process of shortening a set of data computationally."