Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

Utilizing sub-topical structure of documents for information retrieval.

Text segmentation in natural language processing typically refers to the process of decomposing a document into constituent subtopics. Our work centers on the application of text segmentation techniques within information retrieval (IR) tasks. For example, for scoring a document by combining the retrieval scores of its constituent segments, exploiting the proximity of query terms in documents for ad-hoc search, and for question answering (QA), where retrieved passages from multiple documents are aggregated and presented as a single document to a searcher. Feedback in ad hoc IR task is shown to benefit from the use of extracted sentences instead of terms from the pseudo relevant documents for query expansion. Retrieval effectiveness for patent prior art search task is enhanced by applying text segmentation to the patent queries. Another aspect of our work involves augmenting text segmentation techniques to produce segments which are more readable with less unresolved anaphora. This is particularly useful for QA and snippet generation tasks where the objective is to aggregate relevant and novel information from multiple documents satisfying user information need on one hand, and ensuring that the automatically generated content presented to the user is easily readable without reference to the original source document

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Summarization of emergency news articles driven by relevance feedback

Many articles on the same news are daily published by online newspapers and by various social media. To ease news article exploration sentence-based summarization algorithms aim at automatically generating for each news a summary consisting of the most salient sentences in the original articles. However, since sentence selection is error-prone, the automatically generated summaries are still subject to manual validation by domain experts. If the validation step not only focuses on pruning less relevant content but also on enriching summaries with missing yet relevant sentences this activity may become extremely time consuming. The paper focuses on summarizing news articles by means of an itemset-based technique. To tune summarizer performance a relevance feedback given on sentences is exploited to drive the generation of a new, more targeted summary. The feedback indicates the pertinence of the sentences that are already in the summary. Among the words or the word combinations selected by the summarization model, those occurring in sentences with high feedback score represent concepts that may be deemed as particularly relevant. Therefore, they are exploited to drive the new sentence selection process. The proposed approach was tested on collections of newsvarticles reporting emergency situations. The results show the effectiveness of the proposed approach