Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation

Nayeem, Mir Tafseer; Chali, Yllias

doi:10.1145/3132847.3133106

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Finally, sentence fusion was done with a word graph. (Nayeem, Fuad, Chali,2018) [16] proposed a sentence fusion-based abstract generation approach. In this approach, first, a sentence generation model is created which performs both the sentence fusion and paraphrasing by skip-gram word embedding model.…”

Section: Sentence Fusion Using Syntactic Representation and Text Gene...mentioning

confidence: 99%

Sentence Fusion using Deep Learning

Roy Chowdhury,

Sarkar

2023

EAI Endorsed Trans IoT

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The human process of document summarization involves summarizing a document by sentence fusion. Sentence fusion combines two or more sentences to create an abstract sentence. Sentence fusion is useful to convert an extractive summary to an abstractive summary. The extractive summary contains a set of salient sentences selected from a single document or multiple related documents. Redundancy creates problems while creating an extractive summary because it contains sentences whose segments or phrases are redundant. Sentence fusion helps to remove redundancy by fusing sentences into a single abstract sentence. This moves an extractive summary to an abstractive summary. In this paper, we present an approach that uses a deep learning model for sentence fusion. which is trained over a large dataset. We have tested our approach through both manual evaluation and system evaluation. The result of our proposed approach shows that our model is good enough to fuse sentences effectively.

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Section: Sentence Fusion Using Syntactic Representation and Text Gene...mentioning

confidence: 99%

Sentence Fusion using Deep Learning

Roy Chowdhury,

Sarkar

2023

EAI Endorsed Trans IoT

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“…In addition, on the one hand, summaries can be extractives, which are generated using only the information included in the original document, such as words, sentences or paragraphs; on the other hand, summaries can be abstract [14], [15], which could convey new information not included in the original document, by commonly combining a paraphrasing process with a language model.…”

Section: Related Workmentioning

confidence: 99%

Toward the Automatic Generation of an Objective Function for Extractive Text Summarization

2023

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A fitness function is a type of objective function that quantifies the optimality of a solution; the correct formulation of this function is relevant, in evolutionary-based ATS systems, because it must indicate the quality of the summaries. Several unsupervised evolutionary methods for the automatic text summarization (ATS) task proposed in current standards require authors to manually construct an objective function that guides the algorithms to create good-quality summaries. In this sense, it is necessary to test each fitness function created to measure its performance; however, this process is time consuming and only a few functions are analyzed. This study proposes the automatic generation of heuristic functions, through genetic programming (GP), to be applied in the ATS task. Therefore, our proposed method for ATS provides an automatically generated fitness function for cluster-based unsupervised approaches. The results of this study, using two standard collections, demonstrate to automatically obtain an orientation function that leads to good quality abstracts.

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“…The domain of summarization is diverse in the scenario of different applications. One of the earliest approach focuses on summarizing a single document [18] then extended to summarize multiple documents [19], email thread [20], recognize the specific online arguments and dialogues [21], [22], and timeline summarization [23]. Comments summarization in social media concentrates on determining which posts are most relevant to a particular topic.…”

Section: Introductionmentioning

confidence: 99%

Summarizing twitter posts regarding COVID-19 based on n-grams

Alabid,

Naseer

2023

IJEECS

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The COVID-19 pandemic announced by the World Health Organization has disrupted human lives at different scales, including the economy, public health, and people's emotions. Social media databases record huge accumulated information concern this pandemic. Twitter platform is considered one of the most active social media that enable users to tweet in different conversations they are concerned about. The problem arises when tweeters want to search about a specific topic. They can only sort tweets by its recency to understand conversation and not by relevancy. This makes tweeters read through the most tweets to understand what was firstly discussed about the related topic. Some strategies were developed for summarizing tweets but summarizing topics of COVID-19 are still at the beginning. The current research aims to introduce a technique to present a short summary related COVID-19 topics with consuming little time and effort. Thus, summarization task started by clustering topics based on latent dirichlet allocation (LDA) method and K-means clustering and then selected the important sentences to format summarization. The study also compares bigram-based and unigram-based summarization. Different metrics were used to evaluate results and experiments at each stage, and the output of the proposal system was evaluated using ROUGE metrics.

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Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation

Cited by 8 publications

References 9 publications

Sentence Fusion using Deep Learning

Sentence Fusion using Deep Learning

Toward the Automatic Generation of an Objective Function for Extractive Text Summarization

Summarizing twitter posts regarding COVID-19 based on n-grams

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