MSCSO: Extractive Multi-document Summarization Based on a New Criterion of Sentences Overlapping

Khaleghi, Zeynab; Fakhredanesh, Mohammad; Hourali, Maryam

doi:10.1007/s40998-020-00361-1

Cited by 5 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patent feature extractor relies on the hand-crafted features that correspond to syntactic and semantic information of patent document sentences. Many of these features are widely used by summarizers for sentence selection [38,40,[65][66][67], and their measures are normalized in the range of 0 to 1 for practical usage.…”

Section: Rbm-based Extractive Patent Summarizermentioning

confidence: 99%

PQPS: Prior-Art Query-Based Patent Summarizer Using RBM and Bi-LSTM

Kumaravel

Sankaranarayanan

2021

Mobile Information Systems

View full text Add to dashboard Cite

A prior-art search on patents ascertains the patentability constraints of the invention through an organized review of prior-art document sources. This search technique poses challenges because of the inherent vocabulary mismatch problem. Manual processing of every retrieved relevant patent in its entirety is a tedious and time-consuming job that demands automated patent summarization for ease of access. This paper employs deep learning models for summarization as they take advantage of the massive dataset present in the patents to improve the summary coherence. This work presents a novel approach of patent summarization named PQPS: prior-art query-based patent summarizer using restricted Boltzmann machine (RBM) and bidirectional long short-term memory (Bi-LSTM) models. The PQPS also addresses the vocabulary mismatch problem through query expansion with knowledge bases such as domain ontology and WordNet. It further enhances the retrieval rate through topic modeling and bibliographic coupling of citations. The experiments analyze various interlinked smart device patent sample sets. The proposed PQPS demonstrates that retrievability increases both in extractive and abstractive summaries.

show abstract

Section: Rbm-based Extractive Patent Summarizermentioning

confidence: 99%

PQPS: Prior-Art Query-Based Patent Summarizer Using RBM and Bi-LSTM

Kumaravel

Sankaranarayanan

2021

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Automatic summarize techniques have been used in search engines, websites, media, and other sorts of online evaluations [2,3]. To summarize documents and news for deeper analysis, a summarize task is used by educational institutions, media outlets, the military, and political entities [4].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Aided Improved LSTM for Multi-document Summarization: A Hybrid Optimization Model

Ketineni,

2023

JWE

View full text Add to dashboard Cite

Multi-document summarization (MDS) is an automated process designed to extract information from various texts that have been written regarding the same subject. Here, we present a generic, extractive, MDS approach that employs steps like preprocessing, feature extraction, score generation, and summarization. The input text goes preprocessing steps such as lemmatization, stemming, and tokenization in the first stage. After preprocessing, features are extracted, including improved semantic similarity-based features, term frequency-inverse document frequency (TF-IDF-based features), and thematic-based features. Finally, an improved LSTM model will be proposed to summarize the document based on the scores considered under the objectives such as content coverage and redundancy reduction. The Blue Monkey Integrated Coot Optimization (BMICO) algorithm is proposed in this paper for fine-tuning the optimal weight of the LSTM model that ensures precise summarization. Finally, the suggested BMICO’s effectiveness is evaluated, and the outcome is successfully verified.

show abstract

A Comprehensive Study on Multi Document Text Summarization for Bengali Language

Nipa,

Sultana

2022

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

MSCSO: Extractive Multi-document Summarization Based on a New Criterion of Sentences Overlapping

Cited by 5 publications

References 34 publications

PQPS: Prior-Art Query-Based Patent Summarizer Using RBM and Bi-LSTM

PQPS: Prior-Art Query-Based Patent Summarizer Using RBM and Bi-LSTM

Metaheuristic Aided Improved LSTM for Multi-document Summarization: A Hybrid Optimization Model

A Comprehensive Study on Multi Document Text Summarization for Bengali Language

Contact Info

Product

Resources

About