Multi-Topic Multi-Document Summarization

Summarization of multiple documents featuring multiple topics is discussed. The example trea.ted here consists of fifty articles about the Peru hostage incident tbr ])ecember 1996 through April 1997. They include a. lot of topics such as opening, negotiation, ending, and so on. The method proposed in this paper is based on spreading activation over documents syntactically and semantically annotated with GI)A (Global l )ocument Annotation) tags. The method extracts important documents aald impor tant parts therein, and creates a network consisting of important entities and relations among them. It also identifies cross-document coreferences to replace expressions with more concrete ones. The method is essentially multi~ lingua] due to the language-independence of the GDA tagset. This tagset can provide a standard fornm.t tbr the s tudy on the transfbrmation and /or generation stage of summarizat ion process, among other natural language processing tasks. 1 I n t r o d u c t i o n A large ('.vent consists of a, number of smaller events. These component events are usually related trot such relations may not be strong enough to define larger topics. For example, a war may consist of opening, battles, negotiations, and so on. These relatively independent events are considered to be topics by themselves and would accordingly be reported in multiple news re'titles. Summarization of such a large event, or multiple documents about multiple topics, is the concern of this paper. Summarization of multiple documents containing nmltiple topics is an unexplored research issue. Some previous studies on summarization (McKeown and Radev, 1995; Barzilay et al., 1999; Mani and Bloedorn, 1999) deal with multiple docmnents about a single topic, but not about multiple topics 1. In order to smnmarize lnultiple docmne, nts with multiple topics, one needs a general, semantics-oriented method for evaluating importance. Summarization of a single document may largely exploit the doculnent structure. As an extreme example, the first paragraph of a newspaper article often serves as a smmnary of the entire article. On the other hand, summa.rization of multiple, documents in general must be more based on their semantic structures, because the, re is no overall consistent document structure across them. Selection of multiple important topics (not keywords) tbr nmltiple-topic summarization has not; yet been really addressed in the previous literatm:e. The present paper proposes a method, based on spreading a.ctivation, for extracting important topics and important documents. Another method proposed which is usefifl for grasping the overview of nlultiple documents is visualization of important entities mentioned and relationships among them. Visualization of relationships among keywords has been studied in the context of information retrieval (Niwa et al., 1997; Sanderson and Croft, ] 999), but to the authors' knowledge the present s tudy is the first to address such visualization in the context of summm'ization. Of conrse a. concise summary of the entire set of multiple doculnents can be obtained by recovering sentences from important entities and their relationships ~s demonstrated in section 3.3. The present s tudy assumes documents annota ted with GDA (Global Document Annota1Maybury (1999) discusses smnmarlzation of multiple topics, but in his study the smmnaries are made ffonl an event database lint not fl'om documents.