Integrating Rhetorical-Semantic Relation Models for Query-Focused Summarization

We present our recent work on query-focused summarization, focusing on our efforts in building and applying models of rhetorical-semantic relations (RSRs) such as contrast and causality. We overview ongoing work in extracting and evaluating RSR models. We describe our system for query-focused summarization, focusing on an enhanced, feature-based framework. We present results of experiments to measure the impact of both RSR and other features on selection and ordering of summary content. We conclude with an overview of results from the official DUC06 evaluation

Building and Refining Rhetorical-Semantic Relation Models

We report results of experiments which build and refine models of rhetorical-semantic relations such as Cause and Contrast. We adopt the approach of Marcu and Echihabi (2002), using a small set of patterns to build relation models, and extend their work by refining the training and classification process using parameter optimization, topic segmentation and syntactic parsing. Using human-annotated and automatically-extracted test sets, we find that each of these techniques results in improved relation classification accuracy

Answering Definitional Questions: A Hybrid Approach

We present DefScriber, a fully implemented system that combines knowledge-based and statistical methods in forming multi-sentence answers to open-ended definitional questions of the form, "What is X?" We show how a set of definitional predicates proposed as the knowledge-based side of our approach can be used to guide the selection of definitional sentences. Finally, we present results of an evaluation of definitions generated by DefScriber from Internet documents

DefScriber: a hybrid system for definitional QA

The implementation of DefScriber, a system for question answering (QA) over the internet was discussed. DefScriber generates multi-sentence definitions to answer questions from internet documents, using an innovative combination of goal-driven and data-driven techniques. The goal-driven part of the system uses a set of definitional predicates to identify types of information which should ideally be included in a definition. DefScriber lets users pose questions interactively via its web interface with robust methods that guarantee that a dynamic definition will be generated for any term contained in an internet document

IBM’s PIQUANT II in TREC2004

PIQUANT II, the system we used for TREC 2004, is a completely reengineered system whose core functionalities for answering factoid and list questions remain largely unchanged from previous. We continue to address these questions using our multi-strategy and multi-source approach

Columbia University at DUC 2004

We describe our participation in tasks 2, 4 and 5 of the DUC 2004 evaluation. For each task, we present the system (s) used, focusing on novel and newly developed aspects. We also analyze the results of the human and automatic evaluations

MEAD - A Platform for Multidocument Multilingual Text Summarization

This paper describes the functionality of MEAD, a comprehensive, public domain, open source, multidocument multilingual summarization environment that has been thus far downloaded by more than 500 organizations. MEAD has been used in a variety of summarization applications ranging from summarization for mobile devices to Web page summarization within a search engine and to novelty detection

From definitions to complex topics: Columbia university at DUC-2005

We describe our approach for the DUC 2005 topicfocused summarization task by adapting a system initially designed to answer only definitional and biographical questions (i.e. “What/Who is X?”). We present a system overview, focusing on new adaptations and developments. We evaluate our performance and propose a novel combined metric that sythesizes a single score from the various metrics provided by NIST. We conclude with general observations about this year’s task and the direction of future work at Columbia