X-STREAMS: Cross- Stream Textual Realtime Multi-document Summarizer Navy SBIR FY2012.1 Sol No.: Navy SBIR FY2012.1 Topic No.: N121-078 Topic Title: X-STREAMS: Cross- Stream Textual Realtime Multi-document Summarizer Proposal No.: N121-078-1104 Firm: Manifest Labs, Inc. 2900 W. Anderson Lane C-200-301 Austin, Texas 78757-1159 Contact: Stephen Hilderbrand Phone: (512) 461-1978 Web Site: www.manifestlabs.com Abstract: In response to the Navy's N121-078 solicitation, Manifest Labs, Inc., proposes X-STREAMS, a real-time summarization system that improves upon the current state-of-the-art results on the novel information reporting of entities and events found in textual data sources. Using a novel combination of mature techniques, and a new semantic layering methodology, X-STREAMS will increase the value of streaming document summarization capabilities, by merging information across streams and improving the timeliness and accuracy of automated knowledge discovery. The ultimate goal of the X-STREAMS research is to automate much of the summarization of documents and other forms of communication which may be represented as text, such as IM chat, voice and image transcriptions. The strength of X-STREAMS is that it uses a data-driven, unsupervised learning approach to train adaptable summarization models. These models can be trained in any language, and do not require special rules or linguists to develop or maintain the system. To minimize redundant information in reports, X-STREAMS employs a parallelized implementation of the leading methodology for determining the maximum marginal relevance in automated document summarization. Benefits: The X-STREAMS SBIR effort will produce an automated multi-document summarization and presentation system that will serve as the primary automated information awareness component in operational scenarios. In this manner, X-STREAMS represents a force multiplier for analysts responsible for reporting on activities occurring within their areas of responsibility, streamlining the commander's information collection needs. X-STREAMS will replace much of the manual reading activities that analysts currently perform, as well as legacy semi-automated systems, minimizing the underreporting that occurs during periods of intense operational activity. X-STREAMS will significantly improve current, state of the art capacity for persistent information awareness with reduced manpower, allowing analysts and soldiers to focus on deeper analysis tasks that only humans perform well, such as higher-level cognitive functions and asking the right questions to drive further analysis. The strength of X-STREAMS is that it uses a data-driven, unsupervised learning approach to train adaptable summarization models. These models can be rapidly trained in any language, and do not require special rules or experts to develop or maintain the system. The UI will enable analysts to trace information back to the source documents to assist in corroboration or conflicting intelligence resolution, as well as to provide iterative feedback on the system to drive improvements. Return