Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

Integrating Document Clustering and Topic Modeling

Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local topics specific to each cluster and global topics shared by all clusters. In this paper, we propose a multi-grain clustering topic model (MGCTM) which integrates document clustering and topic modeling into a unified framework and jointly performs the two tasks to achieve the overall best performance. Our model tightly couples two components: a mixture component used for discovering latent groups in document collection and a topic model component used for mining multi-grain topics including local topics specific to each cluster and global topics shared across clusters.We employ variational inference to approximate the posterior of hidden variables and learn model parameters. Experiments on two datasets demonstrate the effectiveness of our model.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013

Text-to-Pictogram Summarization for Augmentative and Alternative Communication

Many people suffer from language disorders that affect their communicative capabilities. Augmentative and alternative communication devices assist learning process through graphical representation of common words. In this article, we present a complete text-to-pictogram system able to simplify complex texts and ease its comprehension with pictograms

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Studi Awal Peringkasan Dokumen Bahasa Indonesia Menggunakan Metode Latent Semantik Analysis dan Maximum Marginal Relevance

Pertumbuhan informasi online mengalami peningkatan yang signifikan pada dewasa ini. Peningkatan informasi ini memerlukan suatu mekanisme yang mampu menyajikan informasi secara efektif. Salah satu solusi yang ditawarkan adalah melakukan peringkasan teks secara otomatis tanpa menghilangkan konten dan makna dari dokumen. Pada penelitian ini dilakukan penggabungan metode Latent semantic analysis dan metode Maximum marginal relevance untuk proses peringkasan multi-dokumen, sehingga menghasilkan suatu ringkasan dokumen yang tetap mengandung informasi yang dianggap penting dan mewakili topik dari dokumen yang diringkas tersebut