An Architecture For Distributed Natural Language Summarization

We present a system that incorporates agentbased technology and natural language generation to address the problem of natural language summarization of live sources of data. The input to the system includes newswire and on-fine databases and ontologies. The output consists of short summaries that convey information selected to fit the user's interests, the most recent news updates, and historical information. The system is under development. 1 I n t r o d u c t i o n One of the major problems with the Internet is the abundance of information and the difficulty for the average computer user to read everything existing on a specific topic. There exist now more than 100 sources of five newswire in operation on the Internet. The user has to go through megabytes of news every clay to select articles of interest and read the relevant parts of them. Hence, he needs SEARCH AND SELECTION services, as well as for SUMMARIZATION facilities. There currently exist more than 10 operational SEARCH AND SELECTION services on the Web, e.g., DEC's AltaVista [AltaVista 1996]. However, there is little available in the area of S U M M A R I Z A T I O N . The best currently existing Web-based summarization system, N e t s u m m [Preston and Williams 1994], uses a statistical, approach to selecting relevant sentences from an article. It has an impressive user interface, anti is practically domain-independent, but suffers from two major problems: it only summarizes a single article at a time, and it only summarizes articles given by the user, which means that the user has to go through hundreds of articles to select the ones he will send to Netsumm. Other statistical systems [Kupiec et al. 1995], [Rau et al. 1994] have the same characteristics as Netsumm. Another major unsolved problem involves conveying rapidly changing information to the end user in a sensible format. This infor45 mation can come from a multi tude of different sources which use different internal representations to store it. A summarizing program needs to be able to retrieve all this information in real time, process it and produce meaningful summaries in natural language. More specificMly, the innovations that we suggest address some of these problems. A s y n c h r o n o u s s u m m a r i z a t i o n : Synchronous (demand-based) summarization requires that the user needs to know when a new article relevant to his interests has appeared and feed it to the summarizer in order to get a summary back. Such an approach doesn't lead to any economy of time for the user, since he still has to spend time checking whether new articles have been posted and then send them to the summarizer. It wouhl be more efficient for the user to be notified automatically when a new article has been published [Radev 1994] or to be sent a summary of the article directly. Such asynchronous summaries can be based on the specific interests of the user, contained in his user profile. They can also be tailored to the user's prior knowledge of the subject or event. E.g., the user will receive an initial announcement about an event and only updates after that point. S u m m a r i z i n g m u l t i p l e a r t i c l e s : All existing statistical summarizers provide summaries of single articles by extracting sentences from them. If such systems were to summarize a series of articles, they would simply process each of them on its own and output the resulting summaries. Such summaries will likely contain a significant amount of repeated information, as do the source articles themselves. Our summarizer works on a set of articles. It can trace the development of an event over time or contradictions in articles from different sources on the same topic. S u m m a r i z i n g m u l t i p l e s o u r c e s : When different sources present exactly the same information, the user clearly needs only have access to one of them. Practically, this assumption doesn't hold, as different sources provide updates from a different perspective and at different times. An intelligent summarizer's task, therefore, would be to at tain as much information from the multiple sources as possible, combine it, and present it in a concise form to the user. For example, if two sources of information report a different number of casuMties in a particular incident, the summarizer will report the contradiction. S y m b o l i c s u m m a r i z a t i o n : An inherent problem to sentence-extraction based summarizers is the lack of fluency in the output . The extracted sentences fit together only in the case they are adjacent in the source document: It is also clear that these sentences weren't meant to serve as summaries. A system that provides a deeper understanding of the message (or set of messages) will have all necessary information to get a fluent surface summary. I n t e r o p e r a b i l i t y : Since a large-sc~le summarization system should monitor multiple sources of news and other data, it has to use a knowledge transmission language in order to coordinate the multiple autonomous sources. In the following section, we will describe our early summarization prototype, SUMMONS [McKeown and Radev 1995]. In the next sections, we will describe our architecture for realtime summarization, as well as our approach to the issues set forth in the current section.