Tweet Summarization of News Articles: An Objective Ordering-Based Perspective

Chakraborty, Roshni; Bhavsar, Maitry; Dandapat, Sourav Kumar; Chandra, Joydeep

doi:10.1109/tcss.2019.2926144

Cited by 23 publications

(9 citation statements)

References 45 publications

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“…Other challenges are implicit in these opinion summarization methods, such as the following: how to retrieve query relevant sentences, how to cover the main topics in the opinionated text set, and how to balance these two requests [29]. Our proposal is addressed to a similar problem, where news articles are used as the relevant focus instead of users' queries, although few approaches dealing with this problem have been identified [10]. For instance, Chakraborty et al reported a method of summarizing news article tweets that initially captures the diverse opinions from the tweets by creating a unique tweet similarity graph, followed by a community detection technique to identify the tweets representing these diverse opinions [10].…”

Section: Related Workmentioning

confidence: 99%

“…Our proposal is addressed to a similar problem, where news articles are used as the relevant focus instead of users' queries, although few approaches dealing with this problem have been identified [10]. For instance, Chakraborty et al reported a method of summarizing news article tweets that initially captures the diverse opinions from the tweets by creating a unique tweet similarity graph, followed by a community detection technique to identify the tweets representing these diverse opinions [10]. Representative keywords of the news articles are extracted to identify related tweets.…”

Section: Related Workmentioning

confidence: 99%

“…However, some researchers consider LDA-and LSA-based approaches to not proprerly model the aspects of the reviews made on the web [3]; instead, clustering text segment approaches have the advantage of keeping the document structure through segments, to capture the semantics of texts [30]. On the other hand, word embedding models [12] (e.g., word2vec [14], Glove [31], and FastText) have been less applied; only a few approaches have been identified [8,10]. A word embedding is a learned representation for text where words that have the same meaning have a similar representation.…”

Section: Related Workmentioning

confidence: 99%

“…Although summaries generated by several of the reported approaches are focused on specific topics [1,8,9], they are generally identified by looking only at the content in opinionated texts, whereas the context that originates the opinions (e.g., news) is not usually taken into account, being this a weakness. A comprehensive summary of the users' reactions concerning a news article can be crucial due to various reasons, such as (1) understanding the sensitivity/importance of the news, (2) obtaining insights about the diverse opinions of the readers regarding the news, and (3) understanding the key aspects that draw the interest of the readers [10]. On the other hand, to integrate both topic-opinion analysis and semantic information can yield satisfactory results in opinion summarization [1].…”

Section: Introductionmentioning

confidence: 99%

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Towards Context-Aware Opinion Summarization for Monitoring Social Impact of News

et al. 2020

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Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the sentiment from contents, whereas summarization aims to condense the most relevant information. However, most of the reported opinion summarization methods are conceived to obtain generic summaries, and the context that originates the opinions (e.g., the news) has not usually been considered. In this paper, we present a context-aware opinion summarization model for monitoring the generated opinions from news. In this approach, the topic modeling and the news content are combined to determine the “importance” of opinionated sentences. The effectiveness of different developed settings of our model was evaluated through several experiments carried out over Spanish news and opinions collected from a real news platform. The obtained results show that our model can generate opinion summaries focused on essential aspects of the news, as well as cover the main topics in the opinionated texts well. The integration of term clustering, word embeddings, and the similarity-based sentence-to-news scoring turned out the more promising and effective setting of our model.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards Context-Aware Opinion Summarization for Monitoring Social Impact of News

et al. 2020

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“…Text summarization is applied for several purposes including news summarization [6,7], opinion or sentiment summarization [8,9], tweet summarization [10], email summarization [11], scientific papers summarization [12,13] and even web page summarization [14]. The latter offers the potential to provide an overview of web pages to devices with limited features.…”

Section: Introductionmentioning

confidence: 99%

WATS-SMS: A T5-Based French Wikipedia Abstractive Text Summarizer for SMS

et al. 2021

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Text summarization remains a challenging task in the natural language processing field despite the plethora of applications in enterprises and daily life. One of the common use cases is the summarization of web pages which has the potential to provide an overview of web pages to devices with limited features. In fact, despite the increasing penetration rate of mobile devices in rural areas, the bulk of those devices offer limited features in addition to the fact that these areas are covered with limited connectivity such as the GSM network. Summarizing web pages into SMS becomes, therefore, an important task to provide information to limited devices. This work introduces WATS-SMS, a T5-based French Wikipedia Abstractive Text Summarizer for SMS. It is built through a transfer learning approach. The T5 English pre-trained model is used to generate a French text summarization model by retraining the model on 25,000 Wikipedia pages then compared with different approaches in the literature. The objective is twofold: (1) to check the assumption made in the literature that abstractive models provide better results compared to extractive ones; and (2) to evaluate the performance of our model compared to other existing abstractive models. A score based on ROUGE metrics gave us a value of 52% for articles with length up to 500 characters against 34.2% for transformer-ED and 12.7% for seq-2seq-attention; and a value of 77% for articles with larger size against 37% for transformers-DMCA. Moreover, an architecture including a software SMS-gateway has been developed to allow owners of mobile devices with limited features to send requests and to receive summaries through the GSM network.

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