Automatic summarization

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. 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. On the other hand, visual content can be summarized using computer vision algorithms. 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-sh

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (100)

Related publications

Related people (11)

Related people

Related units (3)

Related units

Related concepts (11)

Related concepts

Related courses (8)

Related courses

Related lectures (12)

Related lectures

Natural language processing

Natural language processing (NLP) is an interdisciplinary subfield of linguistics and computer science. It is primarily concerned with processing natural language datasets, such as text corpora or sp

Artificial intelligence

Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of human beings or animals. AI applications include advanced web search engines (e.g., Google

Text mining

Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by a

SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage

Jonathan May

With the increasing democratization of elec-tronic media, vast information resources areavailable in less-frequently-taught languagessuch as Swahili or Somali.That informa-tion, which may be crucially important andnot available elsewhere, can be difficult formonolingual English speakers to effectivelyaccess. In this paper we present SARAL, anend-to-end cross-lingual information retrieval(CLIR) and summarization system for low-resource languages that 1) enables Englishspeakers to search foreign language reposito-ries of text and audio using English queries,2) summarizes the retrieved documents in En-glish with respect to a particular informa-tion need, and 3) provides complete transcrip-tions and translations as needed. The SARALsystem achieved the top end-to-end perfor-mance in the most recent IARPA MATERIALCLIR+summarization evaluations.

2019

ClusterRank: A Graph Based Method for Meeting Summarization

Nikhil Garg

This paper presents an unsupervised, graph based approach for extractive summarization of meetings. Graph based methods such as TextRank have been used for sentence extraction from news articles. These methods model text as a graph with sentences as nodes and edges based on word overlap. A sentence node is then ranked according to its similarity with other nodes. The spontaneous speech in meetings leads to incomplete, informed sentences with high redundancy and calls for additional measures to extract relevant sentences. We propose an extension of the TextRank algorithm that clusters the meeting utterances and uses these clusters to construct the graph. We evaluate this method on the AM I meeting corpus and show a significant improvement over TextRank and other baseline methods.

Isca-Inst Speech Communication Assoc, C/O Emmanuelle Foxonet, 4 Rue Des Fauvettes, Lieu Dit Lous Tourils, Baixas, F-66390, France2009

ClusterRank: A Graph Based Method for Meeting Summarization

Nikhil Garg

This paper presents an unsupervised, graph based approach for extractive summarization of meetings. Graph based methods such as TextRank have been used for sentence extraction from news articles. These methods model text as a graph with sentences as nodes and edges based on word overlap. A sentence node is then ranked according to its similarity with other nodes. The spontaneous speech in meetings leads to incomplete, illformed sentences with high redundancy and calls for additional measures to extract relevant sentences. We propose an extension of the TextRank algorithm that clusters the meeting utterances and uses these clusters to construct the graph. We evaluate this method on the AMI meeting corpus and show a significant improvement over TextRank and other baseline methods.

Idiap2009

EE-608: Deep Learning For Natural Language Processing

The Deep Learning for NLP course provides an overview of neural network based methods applied to text. The focus is on models particularly suited to the properties of human language, such as categorical, unbounded, and structured representations, and very large input and output vocabularies.

CS-422: Database systems

This course is intended for students who want to understand modern large-scale data analysis systems and database systems. It covers a wide range of topics and technologies, and will prepare students to be able to build such systems as well as read and understand recent research publications.

CS-450: Advanced algorithms

A first graduate course in algorithms, this course assumes minimal background, but moves rapidly. The objective is to learn the main techniques of algorithm analysis and design, while building a repertory of basic algorithmic solutions to problems in many domains.