# Presentation: 035258

## GISTexter: A System for Summarizing Text Documents

**Sanda Harabagiu, Dan Moldovan, Paul Morarescu, Finley Lacatusu, Rada Mihalcea, Vasile Rus** and **Roxana Girju**

- Department of Computer Sciences
- The University of Texas at Austin
- Austin TX 78712-1188

- Department of Computer Science
- The University of Texas at Dallas
- Richardson TX 75083-0688

- Dept. of Computer Science & Engr.
- Southern Methodist University
- Dallas TX 75275-0122

- Language Computer Corporation
- 6440 N. Central Expressway
- Dallas TX 75206

## Outline

- 1. Background
- 2. System Architecture
- 3. Single-Document Summaries
- 4. Multi-Document Summaries
- 5. Results and Conclusions

## GISTexter

*Generating summaries as similar as possible to *
*	**human-written abstracts**.*

*Two assumptions:*
_*Single-document summaries*_*: extract the **same** information a human would*
*	**	**consider when writing an abstract of the same document.*
_*Multi-document summaries*_*: capture textual information **shared** across the *
*	**	**document set.*

## Our interest

***Multi-Document Summaries applicable to Question-Answering***

_*Enables the usage of  IE technology !!!*_

_*Need*_* domain information*
*	**** **use CICERO – for topics that are already encoded in it*
*	**** **develop a back-up solution:*
*	**	**- gisting information by combining cohesion and *
*	**	**  **coherence indicators for sentence extraction.*

## What is gisting?

*an activity in which the information taken into account *
*	**is less that the full information content available**.*

_*Empirical Principles*_*: **Named Entities** common to the set of documents*
*are anchors for argument structures that act like ad-hoc templates.*
*Sometimes **cue phrases** indicate coherence with some related information*
*that should be gleaned in the summary.*

## CICERO Information ExtractionSystem

- Extraction

- Sentence
- Compression

- Summary
- Reduction

- Single-
- Document
- Decompo-
- sition

- Cohesive and
- Coherence-
- Based
- Extraction

- Extraction
- Templates

- Content-Based
- Planning and
- Generation

- Multi-Document
- Decomposition

- WordNet

- Tokenizer + Preprocessor
- Named-Entity Recognizer
- Part-of-speech
-     Disambiguator
- Named-Entity Alias
-     Recognition
- Phrasal Parser
- Combiner
- Entity Coreference
- Domain-Event Recognizer
- Domain Coreference
- Merging of Event
-     Information

- CICERO Information ExtractionSystem

- Multi-Document Summarizer

- Single-Document Summarizer

- Input article

- Input article set

- No

- Yes

- Summary

- GISTexter

## CICERO: Technical Details

- World knowledge incorporated –
-                     the implementation infrastructure:

- Template Object

- Person

- Location

- Organization

- Money

- Measures

- Date

- Merge
- Template

- Document
- Template

- state
- template

- transition
- template

- - compare
- - merge

## Novelties introduced by Cicero

- Incorporation of World Knowledge:
- extend IE paradigm with linguistic patterns that are non-deterministic and capable of handling ambiguities (unlike lex & yacc)
- conceive a language that extends the C/C++ Integration
- Qualitative improvements
- usage of a full parser that is fast enough
- improved grammars

## Novelties introduced by Cicero (cont’d)

- Ease of Use and Customization
- explicit domain and combiner rules
- minimalist rules that are expanded by built-in compiler.
- Quantitative improvements
- unprecedented F-measures: 78.8%
- unprecedented speed average:
- 2.2 seconds/document.

## The Organization of Knowledge in CICERO

## New Domain: Natural Disasters

- Think of new words characteristic of the domain
- Nouns: tornado
- 			flood
- 			Ice Storm
- Verbs:  happen
- 			fear
- 			rip
- 			hit

## World Knowledge

- What causes disasters?
- Nouns: ice, storm
- Verbs:  bring, raise

- How are people affected?
- Verbs:  injure,  evacuate

- Why Natural Disasters?
- 	- consequences
- Verbs:  cost,  exceed

## Create One New Domain Pattern

- Example: “The tornado ripped through Florida”
**expand{ Active Base**
**	****	**** ****Active Infinitive**
**	****	**** ****Active Relative Subject}**
**with{**
**	****    ****??label = “HAPPENED_IN”**
**	****    ****??head  = $HAPPEN_WORD**
**	****    ****??subj  = in.B (“is Disaster”)**
**	****    ****??obj   = $Absent**
**	****    ****??prep  = “through” | “in”**
**	****    ****??pobj1 = in.type == TYPE.LOCATION**
**	****    ****??pobj2 = $Absent**
**	****    ****??pobj3 = $Absent**
**	****    ****??semantics = cerr<<“APPEND-IN PATTERN FOUND \n”}**

## A New Combiner Pattern

- 		Example: “Flood caused by ice and snow”
**COMPLEX_NG[10] ==> DISASTER_NG;**
**        ****out.cat += #NG;**
**        ****out.B["isDisaster"] = true;;**
**DISASTER_NG ==> #NG[$DISASTER_WORD]:1**
**                ****{ #VG[$CAUSE_WORD, in.tense ==TENSE_PAST] ****	****	****	****"by"**
**                  ****#NG[$DISASTER_CAUSE]**
**                  ****{ "," #NG[$DISASTER_CAUSE] }?**
**                  ****{ ","? "and" #NG[$DISASTER_CAUSE] }?}?;**
**        ****out.item = in(1).item;;**

## Compilation process

- “.g” specification file

- psc

- runtime library

- “.cc” C++ source code file

- g++

- “.o” object file

- g++

- binary file

## Information/Sentence Extraction

_*Sentence Extraction*_*: - learn an extraction function that identifies sentences*
*	**	**	**containing information essential to the summary.*

*METHOD: *_*Abstract decomposition*_
*CASE: Single-document summaries:*
*	**- based on HMMs to assign probabilities and the Viterbi algorithm*
*	**  **to decide the positions (Sentence, Word-in-Sentence) *
*	**(Jing & McKeown SIGIR’99)*

## Decomposition of multiple abstracts

*Maximize similarity to *_*all*_* human-written abstracts**.*

## Interesting Data

- Agreements between pairs of human-written abstracts.

## Sentence Extraction

*Instance-Based Learning (26 features)*

_*Features*_*:*

## Maximal Marginal Relevance

*A measure for quantifying the *_*degree of dissimilarity*_* between the *
*Sentence being considered and the sentences already selected  for*
*Extraction. (Goldstein & Carbonell)*

*Suppose **S** is the set of sentences selected; **R** the set of relevant sentences.*

## Other features

## Ranking Table (Single-Doc)

| G | C | O | T |
| --- | --- | --- | --- |
| R(1) | V(1) | V(1) | V(1) |
| P(2) | R(2) | R(2) | R(2) |
| Q(3) | O(3) | T(3) | O(3) |
| O(4) | T(4) | O(4) | T(4) |
| V(5) | W(5) | P(5) | P(5) |
| S(6) | P(6) | W(6) | Q(6) |
| T(7) | Q(7) | X(7) | W(7) |
| X(8) | X(8) | Y(8) | X(8) |
| W(9) | Y(9) | Q(9) | S(9) |
| Z(10) | S(10) | S(10) | Y(10) |
| Y(11) | Z(11) | Z(11) | Z(11) |
| L(*) | L(*) | L(*) | L(*) |
| M(*) | M(*) | M(*) | M(*) |
| N,U(*) | N,U(*) | N,U(*) | M,U(*) |

## Ranking Table (Multiple-Doc)

| G | C | O | T |
| --- | --- | --- | --- |
| O(1) | T(1) | T(1) | T(1) |
| L(2) | O(2) | R(2) | O(2) |
| P(3) | R(3) | O(3) | R(3) |
| N(4) | M(4) | M(4) | N(4) |
| R(5) | N(5) | N(5) | P(5) |
| S(6) | P(6) | P(6) | M(6) |
| T(7) | S(7) | S(7) | L(7) |
| M(8) | L(8) | Z(8) | S(8) |
| Z(9) | Z(9) | L(9) | Z(9) |
| U(10) | Y(10) | Y(10) | Y(10) |
| W(11) | W(11) | W(11) | W(11) |
| Y(12) | U(12) | U(12) | U(*) |
| Q(*) | Q(*) | Q(*) | Q(*) |
| V,X(*) | V,X(*) | V,X(*) | V,X(*) |

## Details

- _[http://www.seas.smu.edu/~sanda/duc.ps.gz](http://www.seas.smu.edu/~sanda/duc.ps.gz)_
- _[http://www.cs.utexas.edu/users/sanda/duc.ps.](http://www.cs.utexas.edu/users/sanda/duc.ps.gz)__[gz](http://www.cs.utexas.edu/users/sanda/duc.ps.gz)_