code: def get_siblings_content(self, current_sibling, baselinescore_siblings_para):
          
          if current_sibling.tag == 'p' and len(self.parser.getText(current_sibling)) > 0:
              e0 = current_sibling
              if e0.tail:
                  e0 = deepcopy(e0)
                  e0.tail = ''
              return [e0]
          else:
              potential_paragraphs = self.parser.getElementsByTag(current_sibling, tag='p')
              if potential_paragraphs is None:
                  return None
              else:
                  ps = []
                  for first_paragraph in potential_paragraphs:
                      text = self.parser.getText(first_paragraph)
                      if len(text) > 0:
                          word_stats = self.stopwords_class(language=self.language).get_stopword_count(text)
                          paragraph_score = word_stats.get_stopword_count()
                          sibling_baseline_score = float(.30)
                          high_link_density = self.is_highlink_density(first_paragraph)
                          score = float(baselinescore_siblings_para * sibling_baseline_score)
                          if score < paragraph_score and not high_link_density:
                              p = self.parser.createElement(tag='p', text=text, tail=None)
                              ps.append(p)
                  return ps
docs: \
          adds any siblings that may have a decent score to this node