code: def get_siblings_content(self, current_sibling, baselinescore_siblings_para):
          
          if current_sibling.tag == 'p' and self.parser.getText(current_sibling):
              tmp = current_sibling
              if tmp.tail:
                  tmp = deepcopy(tmp)
                  tmp.tail = ''
              return [tmp]
          else:
              potential_paragraphs = self.parser.getElementsByTag(current_sibling, tag='p')
              if potential_paragraphs is None:
                  return None
  
              paragraphs = list()
              for first_paragraph in potential_paragraphs:
                  text = self.parser.getText(first_paragraph)
                  if text:  # no len(text) > 0
                      word_stats = self.stopwords_class(language=self.get_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:
                          para = self.parser.createElement(tag='p', text=text, tail=None)
                          paragraphs.append(para)
              return paragraphs
docs: adds any siblings that may have a decent score to this node