\documentclass[oribibl]{llncs}

\usepackage{epsfig}

\begin{document}

\title{Cohesive Generation of Syntactically Simplified Newspaper Text}

\author{Yvonne Canning \and John Tait \and Jackie Archibald \and Ros
  Crawley}

\institute{School of Computing, Engineering \& Technology\\
  University of Sunderland\\
  St. Peter's Campus\\
  Sunderland\\
  UK\\[5mm]
\email{yvonne.canning@sunderland.ac.uk}
  }

\maketitle

\begin{abstract}

This paper describes SYSTAR (SYntactic Simplification of Text for
Aphasic Readers), the PSet module which splits compound sentences,
activises seven agentive passive clause types, and resolves and
replaces eight frequently occurring anaphoric pronouns. We describe
our techniques and strategies, report on the results obtained after
evaluation of a corpus of 100 newspaper articles downloaded from the
website of a daily provincial, and briefly report on experimental
studies with aphasic participants.

\end{abstract}

\section{Introduction}

In this paper we will describe SYSTAR (SYntactic Simplification of
Text for Aphasic Readers), a system which we believe demonstrates that
general text simplification which retains the meaning and coherence of
original text is feasible. SYSTAR is part of PSet (Practical
Simplification of English Text)\footnote{PSet is a 3-year project
  funded by UK Engineering \& Physical Sciences Research Council
  (Grant No: GR/L50105)}, a system which lexically and syntactically
simplifies newspaper text for aphasic readers~\cite{carroll98}.

Aphasia is an acquired language impairment which may follow brain
damage caused by, for example, head injury or stroke. Those affected
were previously normal language-users and intellect is not affected.
Research has shown that certain constructions may be difficult for
aphasic readers to comprehend~\cite{lesser78}. For instance passive
clauses can be problematic~\cite{caplan86}, though world knowledge may
enable interpretation of semantically non-reversible clauses; the
resolution of anaphors can be difficult~\cite{caspari98}; and the
complexity of sentences is increased by multiple verbs and their
functional argument structures~\cite{byng88}. Accordingly, SYSTAR
splits compound sentences, activises some passive clauses, and
resolves and replaces eight anaphoric pronouns.  SYSTAR's training and
test data were articles taken from a local newspaper's website.


\section{Cohesion}

SYSTAR takes as input text which has been analysed as follows: i) a
tagger~\cite{elworthy94} assigns part of speech (POS) to each word;
ii) verbs and nouns are morphologically analysed; iii) the sentence is
parsed~\cite{briscoe95}, e.g., (S (NP (``A'' AT1) (``cat'' NN1)) (VP (``have+s'' VHZ)
(VPPAS (``be+en'' VBN) (``attack+ed'' VVN) (PP (``by'' II) (NP (``a'' AT1)
(``dog'' NN1)))))).  VPPAS is the Verb Phrase PASsive marker.

SYSTAR splits compound sentences which can mean that what were
within-sentence anaphora cross sentence boundaries, frequently
resulting in sentences that begin with anaphoric pronouns. Splitting
of sentences also entails the removal of conjunctions, which are
sometimes used to facilitate text ``flow''. Activisation of passive
clauses, i.e. change of voice, entails the rearrangement of clauses
within a sentence. When these sorts of changes are made to text, it is
essential that the cohesion of the original text is maintained to
ensure that interpretation of the discourse is not
disrupted~\cite{halliday76}. Also, as Quirk et al.~\cite{quirk72} caution ``a
shift of meaning may accompany shift of voice''.

SYSTAR maintains cohesion by:

\begin{itemize}
\item Ensuring anaphoric expressions are replaced by the most recent
  noun phrase (NP)
\item Ensuring resolved anaphors are not replaced if the original NP appears previously within the sentence, to avoid ambiguous replacements
\item Replacing original sentence-opening anaphors with NPs to
  maintain text style
\item Preserving the tense, mood and aspect of passive verb phrases
  (VPs)
\end{itemize}



\section{Text Patterns and Simplifications}

SYSTAR's simplification module utilises production rules, the left hand (LH) of which is a simplifiable text pattern; i.e. an ordered description of the significant markers of the input sentence parse.  Unification pattern matching takes place, during which the input sentence is matched against the LH. The right hand (RH) of each rule is a simplified text pattern, an ordered description of the variables corresponding to the required transformed output parse tree; after unification bound tree fragments are substituted into a copy of the RH. Sentences are recursively re-applied to each rule until there is no unification and all rules have been applied, ensuring that multiple instances of a text pattern are correctly processed.

\subsection{Passive Clause Patterns}

Within SYSTAR only agentive passives can be activised. Thus, the
preposition phrase marker (PP) and preposition (``by'' II) are matched
and the subsequent NP is checked to ensure it is not a temporal
expression, e.g. ``by September''.~\cite{quirk72} define six passive
verb clause types: present tense (is attacked); past tense (was
attacked); modal auxiliary verb (should be attacked); perfective
aspect (have been attacked); progressive aspect (was being attacked);
modal + perfective (may have been attacked). To these should be added
the habitual contrast (used to be attacked). Currently there are three
VPPAS rules and these cover the above seven clause types. All text
preceding the passive phrase is bound (?a) and the final NP of that
text, which will be the object of the activised version, is removed
and bound to a new variable (?x). Any text following the passive
phrase is bound separately (?e) to ensure surrounding text is not
affected. Figure~\ref{fig-pattern} shows the modal parse tree,
consisting of two verb groups: modal and primary
auxiliary-and-lexical. However, perfective, progressive and modal +
perfective clauses also unify with this tree and analysis of verbs
within bindings is required to determine the clause type and correct
output. Table~\ref{table-sub-patterns} shows the modal sub-patterns.


\begin{figure}[htbp]
  \begin{center}
  \epsfig{file=tree.eps, width=0.8\textwidth}
  \end{center}
  \caption{General Modal Passive Pattern}
  \label{fig-pattern}
\end{figure}


\begin{table}[htbp]

\begin{center}
\begin{tabular}{|l|c|c|c|l|l|} \hline
\multicolumn{2}{|c|}{\bf (? b)} & \multicolumn{2}{c|}{\bf (? c)}       & \multicolumn{1}{|c|}{\bf Clause Type} & \multicolumn{1}{|c|}{\bf Output Forms}\\ \cline{3-4}
\multicolumn{2}{|c|}{\bf Modal}           & \bf Aux & \bf Lexical         &             & \\ \hline
\it has/have/etc &   & VBN &  VVN            & Perfective  & Modal depends on agent no.\\
          &      & \it been & \it attacked         &             & Lexical unchanged.\\ \hline
\it were/is/etc  &   & VBG &  VVN            & Progressive & Modal depends on agent no.\\
         &       & \it being & \it attacked       &             &  -ing form of lexical.\\ \hline
\it will/can/etc &   & VBO &  VVN            & Modal       & Modal unchanged.\\
        &        & \it be  & \it attacked     &             & Base form of lexical.\\ \hline
\it would/can/etc & VHO & VBN & VVN             & Modal+Perfective & Modal unchanged.\\
             & have   & been & attacked        &               & Lexical unchanged\\ \hline
\end{tabular}

\end{center}

\caption{Modal Passive Clause Sub-patterns}
\label{table-sub-patterns}

\end{table}


\section{Anaphora Resolution and Replacement}

An analysis of pronoun usage in newspaper articles was reported
in~\cite{canning99}. As a result of this analysis it was decided that
eight frequently occurring coreferring anaphors (singular ``he'', ``she'',
``him'' and ``her''; plural ``they''; singular possessives ``his'' and ``her'';
plural possessive ``their'') would be resolved and replaced. The
anaphora resolution algorithm is based on CogNIAC~\cite{baldwin97}.
Each NP is given an ID number and is input to an entity tool which
assigns properties according to phrase-marker, POS-tag or word.
Properties are Gender, Number and EntityType. EntityTypes are:
Company, Organisation, Person and Items, where ``Items'' are other
plural NPs which can be referred to by ``they'' or ``their'', such as ``the
roads''. The entity tool is an array whose cells hold multiple entries.
For instance, the top 88 British surnames, according to 1996 UK
electoral rolls, appear within one cell. There are three fields in the
array, holding: the location of the indicator, the indicator itself,
and the properties associated with it. Indicators are ranked so for
instance honorifics such as ``Princess'' are ranked above names such as
``Mary'', to allow for anomalies such as ``Princess Michael of Kent''.
Once a pronoun is detected, all possible antecedents are recalled. We
resolve according to coreference restrictions and recency only, and
this currently returns 85\% precision. After resolution the chosen
antecedent's ID is attached to the pronoun (4 ``he'' PPSH1), and during
replacement the corresponding NP is substituted.


\section{Evaluation}

To evaluate the system an initial corpus consisting of 100 newspaper
articles was created. The numbers of compound sentences, passive
clauses and anaphors are given in Table 2. From a total of 405 passive
clauses only 60 were activisable; the rest were either agentless or
were incorrectly parsed. From a total of twenty compounds, two were
incorrectly parsed. We believe twenty is an untypically low figure for
this sort of text, but were unable to investigate this further in the
current round of evaluations. The remainder of this section presents
our results.


\subsection{Anaphora Resolution}

Preliminary results of anaphora resolution show recall of 81.5\% with
precision of 85\%, where recall is the percentage of correctly
resolved anaphors from the total to be resolved, and precision is the
percentage of correctly resolved anaphors from the total
resolved. This result is likely to improve as the entity tool's
coverage is increased. Plural anaphors are problematic; at present
50\% are incorrectly resolved. If plurals are not included we achieve
88\% recall and 92\% precision.


\section{Compound Sentence Simplification}

All compound sentences were correctly unified; only one, with an
example of NP ellipsis, gave ungrammatical output: ``He is a football
fanatic and won with the phrase...'' became ``He is a football
fanatic. Won with the phrase...''. This will be solved by the insertion
of the previous subject (He) and will be implemented in a future
version of the system.


\begin{table}[htbp]

\begin{center}
  \begin{tabular}{|l|lr*{3}{@{\hspace{0.12in}}lr}|} \hline    

    \multicolumn{5}{|l}{\bf Total Words:} & \multicolumn{4}{|r|}{69,000}\\ \hline
    \multicolumn{5}{|l}{\bf Compound Sentences} & \multicolumn{4}{|r|}{18}\\ \hline
    \multicolumn{5}{|l}{\bf Activisable VPPAS Clauses} & \multicolumn{4}{|r|}{60}\\ \hline

    \bf Clause Types & Past: & 26 & Habitual Contrast: &
    2 & Progressive: & 4 & Present: & 3 \\
    & Modal: & 11 & Modal + Perfective: & 2 & Perfective: &
    12\makebox[0pt][l]{ (1 negative)} &&\\ \hline    

    \multicolumn{5}{|l}{\bf Anaphoric Pronouns} & \multicolumn{4}{|r|}{655}\\ \hline

    \bf Pronoun Types & 
    He: & 192 & She: & 76 & Him: & 19 & Her: & 5\\
    &    His: & 138 & Possessive Her: & 48 &  They: & 83 & Their: & 94\\ \hline
  
\end{tabular}
\end{center}

\caption{100-Article Corpus Statistics}
\label{table-stats}

\end{table}



\subsection{Passive Clause Activisation}

Approximately 70\% of activised clauses are grammatically
acceptable. The following example demonstrates how meaning can be
changed. The passive's subject (he) is part of a relative clause but
the entire clause is bound. The original sentence was: ``The convicted
football hooligan who claims he is being harassed by the police has
launched a formal complaint.'' SYSTAR's current libellous output is:
``Police are harassing the convicted football hooligan who claims he
has launched a formal complaint''.

\subsection{Experimental Study}

A recent pilot experiment~\cite{devlin99} measured six aphasic participants'
reading time and comprehension of sentence pairs holding
cross-sentence anaphors compared to similar sentences with anaphors
replaced by original NPs. Results showed that replacement shortened
reading time for five out of the six participants and enabled everyone
to correctly answer all questions.


\section{Future Work and Conclusion}

We believe SYSTAR has already demonstrated the feasibility of text
simplification retaining (in general) meaning and cohesion, despite
using only limited linguistic data and no world knowledge.  Of course
current performance falls short of what is required for a practical
application but we have identified ways forward with all problems.
Future work to be done includes the following:

\begin{itemize}
\item Further rules are required to improve precision of anaphora
  resolution
\item Grammatical relations (subject and object) will be included in
  the next version of the parser to aid replacement of elided NPs and
  improve anaphora resolution
\end{itemize}

Section 5 pointed out some limitations of our current work.  However,
all aphasic participants commented on the usefulness and effectiveness
of simplified text~\cite{devlin99} and the simplifications may be
useful to others such as deaf readers~\cite{quigley94}. The fact that
our approach focuses on linguistic knowledge indicates that it should
be comparatively easily applicable to other domains and applications.


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\end{thebibliography}




\end{document}
