Chan, Samuel W.K.

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UC MercedProceedings of the Annual Meeting of the Cognitive Science SocietyTitleTowards a Computational Model of Discourse SummarizationPermalinkhttps://escholarship.org/uc/item/463428m5JournalProceedings of the Annual Meeting of the Cognitive Science Society, 18(0)AuthorChan, Samuel W.K.Publication Date1996 Peer reviewedeScholarship.org Powered by the California Digital LibraryUniversity of CaliforniaToward s a  Computationa l  Mode l  o f  Discours e Summarizatio n Samuel  W.  K .  Cha n Schoo l  o f  Compute r  Scienc e &  Engineerin g Th e Universit y o f  N e w Sout h Wale s N S W 2052 .  Australi a s w k c h a n S c s e . u n s w . e d u . a u A b s t r a c t Understanding a discourse is considered to involve a series of  specifi c  processin g phase s whic h fina l  resul t  i s a  complet e semantic ,  menta l  representatio n (Johnson-Laird .  1983 ;  va n Dij k &  Kintsch .  1983) .  Thi s resul t  i s  no t  onl y a  representa -tio n o f  th e text ,  bu t  rathe r  o f  wha t  th e tex t  i s  about .  W h e n a reade r  i s aske d t o summariz e a  discourse ,  vas t  amount s o f  in -formatio n withi n th e discours e ar e selectivel y ignore d i n or -der  t o produc e a  distille d versio n o f  th e origina l  text .  Thi s simplificatio n proces s emphasize s centra l  element s o f  th e dis -cours e whil e th e periphera l  detail s ar e neglected .  I t  i s furthe r demonstrate d tha t  discours e ca n b e represente d a s a  skeleto n i n whic h th e relationship s amon g th e clause s coul d b e chun -ke d i n a  wa y tha t  replicate d th e semanti c structur e o f  th e orig -ina l  discours e (Gros z &  Sidner ,  1986) .  Textua l  continuity , whic h differentiate s a  tex t  fro m a  rando m sequenc e o f  sen -tences ,  i s a  prim e facto r  i n discours e summarizatio n (Ehrlic h &Charolles ,  1991) . I n thi s paper ,  w e presen t  a  computationa l  mode l  fo r transformin g discourse s int o Quasi-Menta l  Cluster s ( Q M C s ) throug h a  convergenc e process .  Th e proces s i s interprete d a s a particula r  transformatio n o f  a  give n se t  o f  discours e seg -ment s an d concept s b y examinin g th e textua l  continuity .  Be -for e th e process ,  a  cohesio n parsin g i s  first  conducte d i n test -in g th e loca l  cohesio n amongs t  th e sentences .  I t  i s  achieve d i n a constrain t  ne t  whic h i s formulate d a s a  constrain t  satisfactio n proble m ove r  a  se t  o f  finite  element s (Walt z &  Pollack .  1985) . Th e element s i n th e ne t  ma y represen t  words ,  phrases ,  an d mor e importantly ,  th e buffer s whic h ar e designate d t o carr y eac h prio r  analyze d sentence s ove r  int o th e curren t  process -in g cycle ,  i n hop e tha t  the y woul d serv e a s c o m m o n bridg -in g element s betwee n th e sentences .  A n efficien t  filtering  al -gorith m i s employe d t o reduc e th e incohesiv e one s whil e co -hesiv e link s amongs t  sentence s ar e the n defined .  I n th e con -vergenc e process ,  sentence s i n a  discours e ar e represente d a s node s an d connecte d b y th e link s i n a  modifie d Brain-State -in-a-Bo x (BSB )  networ k (Anderso n &  Murphy ,  1986) .  The y ar e highl y interconnecte d an d feedbac k upo n themselves .  Th e B SB networ k operate s b y acceptin g a  patter n o f  activation s and amplifyin g tha t  patter n throug h th e feedbac k loop .  C o m -petin g coalition s o f  th e node s driv e th e networ k int o a  sta -bl e equilibratio n fro m whic h th e Q M Cs ar e extracted .  Th e stronges t  connectio n i n th e Q M Cs wil l  aris e fro m th e pai r  o f node s tha t  maintai n hig h activation s fo r  a  prolonge d perio d o f time .  Th e mode l  i s teste d usin g children' s storie s an d th e re -sult s attes t  it s validity .  Th e representatio n o f  text s i n Q M Cs capture s som e importan t  aspect s o f  th e memor y representa -tio n o f  discourses .  Thus ,  th e resultin g Q M Cs ar e th e use -fu l  dat a structure s i n summarization ,  questio n answerin g an d knowledg e discover y i n discourses . References Anderson ,  J.A. ,  &  Murphy ,  G.L .  (1986) .  Psychologica l  con -cept s i n a  paralle l  system .  Physic a D ,  22 ,  318-336 . Ehriich. M.-F, & Charolles, M. (1991). Aspects of textual continuity :  Psycholinguisti c approaches .  I n G .  Denhier e & J.-P .  Ross i  (Eds.) ,  Tex t  an d Tex t  Processing ,  North-Holland , 269-285 . Grosz,  B.J., & Sidner, C.L. (1986). Attention, intention, and th e structur e o f  discourse .  Computationa l  Linguistics ,  12 , 175-204 . Johnson-Laird, P.N. (1983). Mental Models. Cambridge, M A:  Harvar d Universit y Press . van Dijk, TA.. & Kintsch, W. (1983). Strategies of Discourse Comprehension .  N Y :  Academic . Waltz, D.L.. & Pollack, J.B. (1985). Massively parallel pars-ing :  a  strongl y interactiv e mode l  o f  natura l  languag e inter -pretation .  Cognitiv e Science ,  9 ,  51-74 . 740 

Towards a Computational Model of Discourse Summarization

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Towards a Computational Model of Discourse Summarization

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