Nowadays, we are surrounded by more and more online news articles. Tens or hundreds of news articles need to be read if we wish to explore a hot news event or topic. So it is of vital importance to automatically synthesize a batch of news articles related to the event or topic into a new synthesis article (or overview article) for reader's convenience. It is so challenging to make news synthesis fully automatic that there is no successful solution by now. In this paper, we put forward a novel Interactive News Synthesis system (i.e. INS), which can help generate news overview articles automatically or by interacting with users. More importantly, INS can serve as a tool for editors to help them finish their jobs. In our experiments, INS performs well on both topic representation and synthesis article generation. A user study also demonstrates the usefulness and users' satisfaction with the INS tool. A demo video is available at this https URL .
There are some attempts of adding interaction functions into the traditional document summarization tasks @cite_3 @cite_11 . However, the above work focuses on producing short summaries and the generation of long news overview articles is more challenging. Moreover, in the above work, the keyphrases to represent salient information are extracted based on some heuristic rules or simple clues, and they are usually not good subtopic representations.
@cite_3 @cite_11
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This paper describes the Interactive Document Summariser (IDS). IDS provides dynamic control over document summary characteristics, such as length and topic focus, so that changes made by the user are instantly reflected in an on-screen summary. 'Summary-in-context' views allow users to move flexibly between summaries and their source documents. IDS adopts the technique of sentence extraction, exploiting keyphrases that are automatically extracted from document text as the primary attribute of a sentence extraction algorithm. We report an evaluation of IDS summaries, in which representative end-users of on-line documents identified relevant summary sentences in source documents. IDS summaries were then compared to the recommendations of the users and we report the efficacy of the summaries based on standard precision and recall measures. In addition, using established evaluation metrics we found that IDS summaries were better than baseline summaries based on within-document sentence ordering. We describe iNeATS -- an interactive multi-document summarization system that integrates a state-of-the-art summarization engine with an advanced user interface. Three main goals of the system are: (1) provide a user with control over the summarization process, (2) support exploration of the document set with the summary as the staring point, and (3) combine text summaries with alternative presentations such as a map-based visualization of documents.