Related papers: Uzbek text summarization based on TF-IDF
In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to…
Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words. This research topic has started to attract the attention of a…
Since the advent of the web, the amount of data on wen has been increased several million folds. In recent years web data generated is more than data stored for years. One important data format is text. To answer user queries over the…
Lemmatization is one of the core concepts in natural language processing, thus creating a lemmatization tool is an important task. This paper discusses the construction of a lemmatization algorithm for the Uzbek language. The main purpose…
This paper introduces FeruzaSpeech, a read speech corpus of the Uzbek language, containing transcripts in both Cyrillic and Latin alphabets, freely available for academic research purposes. This corpus includes 60 hours of high-quality…
This paper proposes a text summarization approach for factual reports using a deep learning model. This approach consists of three phases: feature extraction, feature enhancement, and summary generation, which work together to assimilate…
Nowadays, creation of the tagged corpora is becoming one of the most important tasks of Natural Language Processing (NLP). There are not enough tagged corpora to build machine learning models for the low-resource Uzbek language. In this…
Manual Summarization of large bodies of text involves a lot of human effort and time, especially in the legal domain. Lawyers spend a lot of time preparing legal briefs of their clients' case files. Automatic Text summarization is a…
We are developing an automatic method to compile an encyclopedic corpus from the Web. In our previous work, paragraph-style descriptions for a term are extracted from Web pages and organized based on domains. However, these descriptions are…
Today text classification becomes critical task for concerned individuals for numerous purposes. Hence, several researches have been conducted to develop automatic text classification for national and international languages. However, the…
This report describes the MUDOS-NG summarization system, which applies a set of language-independent and generic methods for generating extractive summaries. The proposed methods are mostly combinations of simple operators on a generic…
Text Summarization has been an extensively studied problem. Traditional approaches to text summarization rely heavily on feature engineering. In contrast to this, we propose a fully data-driven approach using feedforward neural networks for…
As Uzbek language is agglutinative, has many morphological features which words formed by combining root and affixes. Affixes play an important role in the morphological analysis of words, by adding additional meanings and grammatical…
This paper describes a computationally inexpensive and efficient generic summarization algorithm for Arabic texts. The algorithm belongs to extractive summarization family, which reduces the problem into representative sentences…
Recent advances in large language models (LLMs) have led to new summarization strategies, offering an extensive toolkit for extracting important information. However, these approaches are frequently limited by their reliance on isolated…
Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR) systems especially with the rapid growth of the number of online documents present in Arabic language. Documents…
Levering data on social media, such as Twitter and Facebook, requires information retrieval algorithms to become able to relate very short text fragments to each other. Traditional text similarity methods such as tf-idf cosine-similarity,…
Abstractive text summarization aims at compressing the information of a long source document into a rephrased, condensed summary. Despite advances in modeling techniques, abstractive summarization models still suffer from several key…
The number of documents available into Internet moves each day up. For this reason, processing this amount of information effectively and expressibly becomes a major concern for companies and scientists. Methods that represent a textual…
Automatic Text Summarization strategies have been successfully employed to digest text collections and extract its essential content. Usually, summaries are generated using textual corpora that belongs to the same domain area where the…