The rapid expansion of social media platforms has resulted in an unprecedented surge short text content being generated on a daily basis. Extracting valuable insights and patterns from this vast volume textual data necessitates specialized techniques that can effectively condense information while preserving its core essence. In response to challenge, automatic summarization (ASTS) have emerged...

Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set. Due to the great challenge of constructing the large scale summaries for full text, in this paper, we introduce a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which will be ...

Understanding the rapidly growing short text is very important. Short text is different from traditional documents in its shortness and sparsity, which hinders the application of conventional machine learning and text mining algorithms. Two major approaches have been exploited to enrich the representation of short text. One is to fetch contextual information of a short text to directly add more...

Message classification is a text classification task that has provoked much interest in machine learning. One aspect of message classification that presents a particular challenge is the classification of short text messages. This paper presents an assessment of applying a casebased reasoning approach that was developed for long text messages (specifically spam filtering) to short text messages...

Ray Bradbury’s The Martian Chronicles is a futuristic, science fiction novel that chronicles the colonization of Mars by humans, projecting the United States’ colonial and immigrant past on to a symbolic future. Bradbury’s use of language is mostly picturesque and sensory. The present paper applies a text-oriented analysis of stylistic elements that construct meaning in the text and evoke the n...

Short documents are typically represented by very sparse vectors, in the space of terms. In this case, traditional techniques for calculating text similarity results in measures which are very close to zero, since documents even the very similar ones have a very few or mostly no terms in common. In order to alleviate this limitation, the representation of short-text segments should be enriched ...

Everyday, millions of short-texts are generated for which effective tools for organization and retrieval are required. Because of the tiny length of these documents and of their extremely sparse representations, the direct application of standard text categorization methods is not effective. In this work we propose using distributional term representations (DTRs) for short-text categorization. ...

Short text clustering has become an increasing important task with the popularity of social media, and it is a challenging problem due to its sparseness of text representation. In this paper, we propose a Short Text Clustering via Convolutional neural networks (abbr. to STCC), which is more beneficial for clustering by considering one constraint on learned features through a self-taught learnin...