Extract sentences from beginning of news in single document summarization
Keselman, Schubert

Computational models for text summarization

The paper deals with methods (models) for text summarization. The reference (base) model was "first sentence model":


  As a baseline for our models we used a trivial model that repeats the
  first sentence of the input document.


Then, various experiments and results are presented, like this one: (notice that "first sentence model" is always present as "baseline")



Moreover, one of the datasets for training and evaluation of models in this paper is DUC, which may be interesting to you.



Steinberger (doctoral Thesis, 2005)

Text Summarization within the LSA Framework

In section 2.1, the author discusses document summarization approaches based on sentence extraction. He identifies five approaches:


Surface Level Approaches
Corpus-based Approaches
Cohesion-based Approaches
Rhetoric-based Approaches
Graph-based Approaches


(The "First Sentence Approach" belongs to the *Surface Level Approaches") The author further describes these approaches and compares them.



Khodra, Widyantoro, Aziz, Trilaksono (Journal of ICT Research and Applications, 2011)

Free Model of Sentence Classifier for Automatic Extraction of Topic Sentences

The author identifies and tests methods for identifying the most important sentences in a text (see list of 58 items below, called features). Surprisingly, in the conclusion, it is said that position of the sentence is a dominant feature, meaning that including all other features into consideration leads only to small improvement.


position
sentence length
number of words before a main verb
adjective incidence
existential there incidence
incidence of 3rd person singular grammatical form
anaphora incidence
coordinators incidence
cardinal number incidence
incidence of past tense endings
Hypernymy
Polysemy
concreteness index
affect_formulai
bad_formulaic
comparison_formulaic
continue_formulaic
contrast_formulaic
detail_formulaic
future_formulaic
gap_formulaic
good_formulaic
here_formulaic
in_order_to_formulaic
method_formulaic
no_textstructure_formulaic
similarity_formulaic
them_formulaic
textstructure_formulaic
tradition_formulaic
us_previous_formulaic
affect
argumentation
better_solution
change
comparison
continue
contrast
interest
need
presentation
problem
research
solution
textstructure
use
copula
aim_ref_agent
gap_agent
general_agent
problem_agent
ref_agent
ref_us_agent
solution_agent
textstructure_agent
them_agent
them_pronoun_agent
us_agent


For you, the most important part of the paper may be table 5:



Read carefully the explanation of the table in the paper, and the whole Section 4.3.



Other papers worth examining:



Luhn (1958)

The Automatic Creation of Literature Abstracts



Kupiec, Pedersen, Chen (1995)

A Trainable Document Summarizer



Yang, Pedersen (1997)

A Comparative Study on Feature Selection in Text Categorization



Sebastiani (2002)

Machine Learning in Automated Text Categorization