A Review on Automatic Text Summarization Approaches

Kumar, Yogan Jaya; Goh, Ong Sing; Basiron, Halizah; Choon, Ngo Hea; Suppiah, Puspalata C

doi:10.3844/jcssp.2016.178.190

Cited by 57 publications

(16 citation statements)

References 67 publications

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“…The Automatic Text Summarization is the process of extracting or collecting important information from original text, and showing as summary [18]. The Summaries according its purpose can be classified as: generic, domain-specific and query-based on the other hand, the summaries according its output can be abstractive or extractive [19].…”

Section: Automatic Text Summarizationmentioning

confidence: 99%

“…Generally, these multiple documents discuss the same topic. Summarization of generic purpose summarizes all documents regardless of topics or domains [19]. While the domain-specific summary focuses on a domain of interest.…”

Section: Automatic Text Summarizationmentioning

confidence: 99%

“…As previous work, we have worked with intelligent training environments for electrical industry where instruction is presented according to particular needs of students [18,19]. In this sense, we have a student model which represents previous knowledge, affective state, personality and learning styles of students [20].…”

Section: Model For Personalized Summarizationmentioning

confidence: 99%

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Towards Personalized Summaries in Spanish based on Learning Styles Theory

Ramírez¹,

Hernández²,

Martínez³

2019

RCS

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Today, advances in information technologies have generated perhaps the largest and fasted exponential growing of electronic texts. On the Internet there are many electronic documents, such as books, technical documents, news articles, blogs, chats, emails and many other digital files. As a result, a user who wants to read and understand this information in a short time will find it a hard task. In this paper, we have conducted an important work in automatic text summarization. Also, we have considered the particular needs of readers. Thus, a model for personalized summarization base on learning styles theory is proposed.

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Section: Automatic Text Summarizationmentioning

confidence: 99%

Section: Automatic Text Summarizationmentioning

confidence: 99%

Section: Model For Personalized Summarizationmentioning

confidence: 99%

See 1 more Smart Citation

Towards Personalized Summaries in Spanish based on Learning Styles Theory

Ramírez¹,

Hernández²,

Martínez³

2019

RCS

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“…Penelitian ini menggunakan konsep ekstraktif, sehingga akurasi dapat dihasilkan dengan membandingkan kalimat-kalimat yang menjadi ringkasan sistem dan ringkasan manual yang divalidasi oleh ahli bahasa. [6][7]…”

Section: Isi Penelitian 21 Landasan Teori 211 Peringkas Teks Otomatisunclassified

Perbandingan Algoritma K-Means Dan K-Nearest Neighbors Pada Sistem Peringkasan Otomatis

Purnamasari

Widiastuti

2017

KOMPUTA

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ABSTRAKPeringkasan suatu dokumen mengambil informasi utama yang terkandung dalam dokumen tersebut. Akan tetapi untuk memperoleh informasi penting yang terkandung di suatu artikel, dibutuhkan waktu yang lama. Hal inilah yang menyebabkan munculnya berbagai penelitian yang berkaitan dengan sistem peringkasan otomatis. Dengan adanya sistem ini, pembaca diharapkan dapat lebih mudah menemukan informasi yang relevan dengan kebutuhannya. K-Means dan K-NN adalah dua buah metode yang telah digunakan untuk meringkas teks secara otomatis. Kedua penelitian yang masingmasing menggunakan metode tersebut, menghasilkan kinerja yang baik (akurasi di atas 50%). Namun, untuk dapat digunakan secara luas, perlu diteliti metode mana yang memiliki akurasi lebih tinggi. Berdasarkan hal tersebut dalam penelitian ini, dilakukan perbandingan metode K-Means dan K-NN dalam kasus peringkasan teks secara otomatis. Dokumen yang digunakan sebagai bahan uji adalah dokumen latar belakang laporan Skripsi.Perbandingan dilakukan dengan menggunakan 100 buah data. Berdasarkan pengujian yang telah dilakukan, peringkasan dengan K-NN menghasilkan rata-rata akurasi sebesar 49%, sementara K-Means sebesar 51%. Hal ini menunjukkan bahwa walaupun K-Means memiliki akurasi yang lebih tinggi, perbedaan keduanya tidaklah mencolok secara umum. Dalam beberapa dokumen, K-NN justru menghasilkan akurasi yang lebih tinggi secara signifikan.

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“…Generally, there are two types of summary which can be generated, that is, extractive and abstractive-based summaries (Kumar, Goh, Halizah, Ngo, & Puspalata, 2016). An extractive summary comprises of the original sentences which are selected from the input document.…”

Section: Introductionmentioning

confidence: 99%

Improving text summarization using neuro-fuzzy approach

Azhari

Kumar

2017

Journal of Information and Telecommunication

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In today's digital era, it becomes a challenge for netizens to find specific information on the internet. Many web-based documents are retrieved and it is not easy to digest all the retrieved information. Automatic text summarization is a process that identifies the important points from all the related documents to produce a concise summary. In this paper, we propose a text summarization model based on classification using neuro-fuzzy approach. The model can be trained to filter high-quality summary sentences. We then compare the performance of our proposed model with the existing approaches, which are based on fuzzy logic and neural network techniques. ANFIS showed improved results compared to the previous techniques in terms of average precision, recall and F-measure on the Document Understanding Conference (DUC) data corpus.

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A Review on Automatic Text Summarization Approaches

Cited by 57 publications

References 67 publications

Towards Personalized Summaries in Spanish based on Learning Styles Theory

Towards Personalized Summaries in Spanish based on Learning Styles Theory

Perbandingan Algoritma K-Means Dan K-Nearest Neighbors Pada Sistem Peringkasan Otomatis

Improving text summarization using neuro-fuzzy approach

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