summarizing documents based on cue-phrases and references

Summarizing documents based on cue-phrases and references

Dan CristeaOana Postolache

Georgiana PuşcaşuLaurenţiu Ghetu

“Alexandru Ioan Cuza” University of Iasi

Romania

Goal: coherent focused summaries

What is a focused summary? - reveals on short what the document tells about the key entity (focus), within the context of the whole document

Why focused summaries? For example, when searching the web about an entity: - avoid browsing tremendous list of links to documents mentioning that entity (as given by a normal search engine) - read abstracts that mention the searched entity- if of minor importance in a document, the searched entity will not appear in a normal abstract

The idea

summary

discourse structure

cue-phrases anaphoric references

VT

The proposed method (1)

Preparatory phases:

- POS-tagging

- Syntactic tagging done by an FDG parser

- NP-tagging


Step 1: segmentation into elementary discourse units (edu-s)

Maria went alone to the market because Simon had to stay at home with the baby. Simon is a good friend of mine and he also helped me in a number of situations. For instance he was very helpful when I had the problem with the car. I think she has a lot of trust in him to let him alone with the child. You know how Maria is: she is not very hurried to give credit to anybody.

1 2

3 45 6

78 9



1 2

3 45 6

78 9

Step 2: building-up sentence level discourse trees (sdt-s)

5

– when –

6

– for instance –


Step 3: anaphora resolution



Step 4: integration of sdt-s in a global structure

foot node

*

pdti-1

sdti

pdti


Step 5: generating the summary

Step 1: Text segmentation method• Identification of finite verbs

• Extraction of the FDG-sub-tree rooted in each finite verb (If FDG tagging is correct, then every sub-tree will represent a clause)

• Grouping clauses, if necessary, into discourse units


Step 2: Inference of the sdt-s (1)

Cue words or phrases (markers)

Inter-edu-s local dependencies

Sentence level discourse trees

Inner nodes labeled with markers

Terminal nodes labeled with edu-s

Step 2: Inference of the sdt-s (2)

Ambiguities:[1] so [2,3,4]because [2,3,4], [2,3,4][1,2] and [3,4] Inferring the sdt = finding the proper arguments and nuclearities

Cue-phrases usually suggest patterns of displacement of the connected arguments, nuclearity included

– so – because –, – – and –

John is determined to pass the NLP exam so, because he has missed many courses and was only vaguely implicated at the working sessions , he will have a hard time until summer.

1 4a 23

4b

1 so, because 2 and 3, 4

Step 2: Inference of the sdt-s (3) Consistency constraints for elementary sdt-s

The “nesting-arguments” ruleIf an edu xsub-tree(markeri) sub-tree(markerj) with ij, then one marker is in the other one’s sub-tree.This rule states that it is impossible to have two inner nodes of the tree, which cover crossing text spans on the terminal frontier

… …

markeri

markerj

Step 3: Anaphora resolutionThe AR-engine

AR-engine is a general framework for anaphora resolution, able to accommodate different AR-models.

AR-engine

text

AR-model3

AR-model2

AR-model1

anaphoric links

The three-layered anaphora resolution process

text layer ……………………….…………………………………………

semantic layer ………………………………………

DEm

REa

projection layer ………………………………………………

DEj DE1

PSx

REb REc REd REx

Reference expressions (RE)

Projected structures (PS)

Discourse entities (DE)

What is an AR-model?

text layer ……………………….…………………………………………

semantic layer ………………………………………

DEm

REa

projection layer ………………………………………………

DEj DE1

PSx

knowledge sources

primary attributes

REb REc REd REx

domain of referential accessibility

heuristics/rules

Types of anaphorae resolved

• Common nouns referring proper nouns

• Common nouns with different lemmas

• Pronominal references

Step 4: Compiling the final discourse structure (1)

A discourse structure tree must be derived by combining the sdt-s

(a = Maria, b = Simon, c = the child, d = I, empty = any other REs)

1 2

because

3 4

and

a b c b b d b d b d a b cd a a

when

for instance

:

b

5 6 7 8 9


1 2

because

a b c

3 4

and

b b d b d

pdt1 = sdt1 sdt2

+ =>1

because

a

3 4

and

b b d b d

2

b c

…

pdt2


b d

when

for instance

5 6

1

because

a

3 4

and

b b d b d

2

b c

… + =>

pdt2 sdt3pdt3

1

because

a

3

and

b b d

2

b c

…

4

b d

b d

when

for instance

5 6


a b cd b

7

1

because

a

3

and

b b d

2

b c

…

4

b d

b d

when

for instance

5 6

+ => 1

because

a

3

and

b b d

2

b c

…

4

b d

b d

when

for instance

5 6

…

a b cd b

7

pdt3 sdt4pdt4


a a

:

8 91

because

a

3

and

b b d

2

b c

…

4

b d

b d

when

for instance

5 6

…

a b cd b

7

+ => 1

because

a

3

and

b b d

2

b c

…

4

b d

b d

when

for instance

5 6

…

a b cd b

7

a a

:

8 9

…

pdt4 sdt5pdt5

Step 5: Generating the summary (1)Veins Theory

• Head expression of a node: the sequence of the most important units within the corresponding span of text:– the head of a terminal node: its label– the head of a non-terminal node: the

concatenation of the head expressions of the nuclear children

• the important units are projected up to the level where the corresponding span is seen as a satellite

Step 5: Generating the summary (2)Computing head expressions

1

because

3

and2

…

4 when

for instance

5 6

…

7 :

8 9

…

H=6

H=9

H=5

H=4

H=2

H=1

H=3 H=8

H=7

H=2

H=8,9

H=3,4

H=4

H=5

H=7

H=2,7

H=1

Step 5: Generating the summary (3)Veins

to understand a piece of text in the context of the whole discourse one needs the significant units within the span together with other surrounding units

Vein expression of a node: the sequence of units that are required to understand the span of text covered by the node, in the context of the whole discourse

Step 5: Generating the summary (4)Computing vein expressions

Vein expressions are computed top-down starting with the root (vein expression of the root is its head expression)

nuclear node with no satellites to the left

satellite node on the right

V=v

V=v

V=v

V=seq(h, v)

V=v

H=h

Step 5: Generating the summary (5)Vein expressions

1

because

3

and2

…

4 when

for instance

5 6

…

7 :

8 9

…V=1,2,7

V=1

V=1,2,3,4,7

V=1,2,7

V=1,2,7

V=1,2,7,8,9V=1,2,3,4,7

V=1,2,7

V=1,2,7

V=1

V=1,2,3,4,7

V=1,2,3,4,7

V=1,2,3,4,5,7 V=1,2,3,4,5,6,7

V=1,2,3,4,5,7

V=1,2,7,8,9V=1,2,7,8,9

The summaries

eduVein expression

1 {1}

2 {1,2,7}

3 {1,2,3,4,7}

4 {1,2,3,4,7}

5 {1,2,3,4,5,7}

6 {1,2,3,4,5,6,7}

7 {1,2,7}

8 {1,2,7,8,9}

9 {1,2,7,8,9}

• Maria is referred in edu-s 1,7,8,9 =>

summary focused on Maria {1,2,7,8,9}

• Simon is referred in edu-s 2,3,4,5,7=>

summary focused on Simon {1,2,3,4,5,7}

• The child is referred in edu-s 2,7 =>

summary focused on the child {1,2,7}

• I is referred in edu-s 3,4,6,7 =>

summary focused on I {1,2,3,4,5,6,7}

Maria went alone to the market because Simon had to stay at home with the baby. I think she has a lot of trust in him to let him alone with the child. You know how Maria is: she is not very hurried to give credit to anybody.

Maria went alone to the market because Simon had to stay at home with the baby. I think she has a lot of trust in him to let him alone with the child.

Results

Segmentation step:The results show that, if the input contained errors made by the FDG parser, the precision and recall of the segmentation method would be around 75%. If the input was corrected (that means if all words were properly related between them), the precision and recall would be of 100%.

Anaphora resolution step:The best results proved 100% precision and values of recall in range 70% to 100%. These figures should be taken with care, because of the small dimension of the corpus we used.

Conclusions

• The method proposed is based on an earlier investigation which showed a correlation between references and vein structure (antecedents can be found along veins - 99,1% references obey this conjecture)• It is a deterministic method in the sense that only one tree is obtained• Degrees of non-determinism show up at:

- building sdt-s due to different cue-phrase patterns- combining sdt-s into a final discourse tree

Further work

• Identify the overall trust in the method• Improve the method of building the global

structure (scores for the types of antecedents)• Transform it by using CT into a beam-search

type of processing• Derive more sophisticated sdt integration rules

by learning• Represent only vein expressions, not the entire

tree

Thank you!

summarizing documents based on cue-phrases and references

Documents

proposed method

discourse units maria

good friend

number of situations

lot of trust

searched entity

anaphora resolution

integration of sdt