An Optimized Semantic Technique for Multi-Document Abstractive Summarization

Khan, Atif; Salim, Naomie; Obasa, Adekunle Isiaka

doi:10.17485/ijst/2015/v8i32/92118

Cited by 5 publications

(6 citation statements)

References 28 publications

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“…This is done by exciting a set of resonators by a voicing source or noise generator to achieve the desired speech spectrum, and by controlling the excitation source to simulate either voicing or voicelessness. [4] The addition of a set of anti-resonators furthermore allows the simulation of nasal tract effects, fricatives and plosives. The specification of about 20 or more such parameters can lead to a satisfactory restitution of the speech signal.…”

Section: Prior Workmentioning

confidence: 99%

Podcast Transcription and Summarization With Speech Synthesis

P.V.

2024

IJSREM

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We propose a system which automatically generate audio summaries for podcasts, allowing listeners to quickly preview podcast episodes. The proposed system first transcribes the audio from a podcast using automatic speech recognition (ASR), then summarizes the transcript using extractive text summarization, and finally returns the audio associated with the text summary. Motivated by a lack of relevant datasets for this task, we created our own by transcribing the audio from various podcasts and generating summaries for these transcripts using a manual annotation tool. After transcription, the text is processed through natural language processing (NLP) algorithms to extract the most important points, key topics, and relevant information from the podcast. The first component of the system involves automatic transcription, where speech-to-text algorithms convert audio podcast episodes into textual format. This transcription process employs state-of-the-art machine learning models trained on large datasets to achieve high accuracy and reliability. The system employs natural language processing (NLP) techniques to generate concise summaries of podcast episodes. These summaries aim to capture the essence of the content, providing users with a quick overview of key topics, insights, and discussions covered in the episode. Key Words: Podcasts, audio summarization, automatic speech recognition, automatic summarization.

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Section: Prior Workmentioning

confidence: 99%

Podcast Transcription and Summarization With Speech Synthesis

P.V.

2024

IJSREM

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“…Để thực hiện những điều này, những nghiên cứu theo hướng tiếp cận này cần phải có sự kết hợp những kỹ thuật và kiến thức thuộc các lĩnh vực về khoa học máy tính là hiểu văn bản và tạo sinh văn bản cũng như các lý thuyết ngôn ngữ học. Trong những năm gần đây, hướng tiếp cận dựa trên "abstractive summarization" bắt đầu được chú ý nhiều hơn với một số phương pháp được đề xuất [1,5,42]: các phương pháp dựa trên tiếp cận cấu trúc "structure-based" như phương pháp cây phụ thuộc [50,51] hay các phương pháp trích xuất thông tin [48]; các phương pháp dựa trên tiếp cận ngữ nghĩa như phương pháp biểu diễn ngữ nghĩa theo những "Information Item" [46] hay đồ thị ngữ nghĩa [27]. Một số vấn đề được đặt ra là những phương pháp này được đề xuất chủ yếu nhằm tóm lược đa văn bản và cũng chưa có sự kết hợp với các lý thuyết ngôn ngữ học.…”

Section: Giới Thiệuunclassified

“…Một số vấn đề được đặt ra là những phương pháp này được đề xuất chủ yếu nhằm tóm lược đa văn bản và cũng chưa có sự kết hợp với các lý thuyết ngôn ngữ học. (Xem [1,5,42]).…”

Section: Giới Thiệuunclassified

Xác định thứ tự thời gian giữa hai câu tiếng Việt chỉ quá trình để tóm lược

Trung¹,

Đăng²

2016

Công nghệ CNTT-TT

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In this paper we introduce a method for summarizing the meaning of two continual Vietnamese sentences manifesting a sequence of processes which belongs to one of three process types (according to Functional Grammar [26, 41]): the state of subject is changed, the position of subject is changed, and the state or position of the subject is affected by an agent. The sentence-generation method is performed in two main processes: (i) resolve anaphoric pronoun and represent the semantics of the source pair of sentences; (ii) determine the ordinal relationship of processes and generate new reduced Vietnamese sentence. To evaluate the quality of summarization, we compare our generated sentences with sentence fusions which generated using K. Filippova [31]’s method as well as an enhancement by F. Boudin and E. Morin [16]. Using ROUGE measures [6 - 9], the results show that our method’s summaries are more precise and natural in overall.

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“…Abstractive summarization creates a summary by paraphrasing and rewriting the text using different words and grammar [ 24 ] and is similar to the manual summarization process used by human experts. The process is more complex than extractive summarization as it involves the use of deep language features and complex NLP processes [ 25 – 28 ]. ATS can be further classified based on the number of input documents into single-document summarization (SDS) and multi-document summarization (MDS).…”

Section: Introductionmentioning

confidence: 99%

Graph-based extractive text summarization method for Hausa text

Bichi,

Samsudin,

Hassan

et al. 2023

PLoS ONE

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Automatic text summarization is one of the most promising solutions to the ever-growing challenges of textual data as it produces a shorter version of the original document with fewer bytes, but the same information as the original document. Despite the advancements in automatic text summarization research, research involving the development of automatic text summarization methods for documents written in Hausa, a Chadic language widely spoken in West Africa by approximately 150,000,000 people as either their first or second language, is still in early stages of development. This study proposes a novel graph-based extractive single-document summarization method for Hausa text by modifying the existing PageRank algorithm using the normalized common bigrams count between adjacent sentences as the initial vertex score. The proposed method is evaluated using a primarily collected Hausa summarization evaluation dataset comprising of 113 Hausa news articles on ROUGE evaluation toolkits. The proposed approach outperformed the standard methods using the same datasets. It outperformed the TextRank method by 2.1%, LexRank by 12.3%, centroid-based method by 19.5%, and BM25 method by 17.4%.

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An Optimized Semantic Technique for Multi-Document Abstractive Summarization

Cited by 5 publications

References 28 publications

Podcast Transcription and Summarization With Speech Synthesis

Podcast Transcription and Summarization With Speech Synthesis

Xác định thứ tự thời gian giữa hai câu tiếng Việt chỉ quá trình để tóm lược

Graph-based extractive text summarization method for Hausa text

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