robust mforrmatlon extractlon, and readlly-avmlable on-hne NLP resources These techtuques and resources allow us to create a richer indexed source of Imgmstlc and domain knowledge than other frequency approaches Our approach attempts to apprommate text dlscourse structure through these multlple layers of mformatlon, ohtinned from automated methods m contrast to labor-lntenslve, discourse-based approaches Moreover, our planned training methodology will also allow us to explmt thin productlve infrastructure m ways whlch model human performance whde avoidmg hand-crafting domain-dependent rules of the knowledge-based approaches Our ultlmate goal m to make our summarlzatlon system scalable and portable by learning summarization rules from easily extractable text features 2 System Description Our summarization system DlmSum consmts of the Summarization Server and the Summarlzatzon Chent The Server extracts features (the Feature Extractor) from a document using various robust NLP techmques, described In Sectzon 2 1, and combines these features (the Feature Combiner) to basehne multiple combinations of features, as described m Section 2 2 Our work m progress to automattcally tram the Feature Combiner based upon user and apphcatlon needs m presented in Section 2 2 2 The Java-based Chent, which wdl be dmcnssed In Section 4, provides a graphical user interface (GUI) for the end user to cnstomlze the summamzatlon preferences and see multiple views of generated sumInarles 2.1 Extracting Stlmmarization Features In this section, we describe how we apply robust NLP technology to extract summarization features Our goal IS to add more mtelhgence to frequencybased approaches, to acqmre domain knowledge In a more automated fashion, and to apprommate text structure by recogmzing sources of dmcourse cohesion and coherence 2.1.1 Going Beyond a Word Frequency-based summarization systems typically use a single word stnng as a umt for counting frequencies Whde such a method IS very robust, it ignores the semantic content of words and their potential membership m multi-word phrases For example, zt does not dmtmgumh between "bill" m "Bdl Table 1 Collocations with "chlps" {potato tortdla corn chocolate b~gle} chips {computer pentmm Intel macroprocessor memory} chips {wood oak plastlc} cchlps bsrgmmng clups blue clups mr chips Clmton" and "bill" in "reform bill" This may introduce noise m frequency counting as the same strmgs are treated umformly no matter how the context may have dmamblguated the sense or regardless of membership in multl-word phrases For DlrnSum, we use term frequency based on tf*Idf (Salton and McGdl, 1983, Brandow, Mitze, and Rau, 1995) to derive ssgnature words as one of the summarization features If single words were the sole basra of countmg for our summarization application, nome would be introduced both m term frequency and reverse document frequency However, recent advances in statmtlcal NLP and information extraction make it possible to utilize features which go beyond the single word level Our approach is to extract multi-word phrases automatlcally with high accuracy and use them as the basic unit in the summarization process, including frequency calculation Ftrst, just as word association methods have proven effective m lemcal analysis, e g (Church and Hanks, 1990), we are exploring whether frequently occurring Collocatlonal reformation can improve on simple word-based approaches We have preprocessed about 800 MB of LA tlmes/Wastnngton Post newspaper articles nsmg a POS tagger (Bnll, 1993) and derived two-word noun collocations using mutual information The.